Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review Article Swipe
Mohammad-Kasim Fassia
,
Adithya Balasubramanian
,
Sungmin Woo
,
Hebert Alberto Vargas
,
Hedvig Hricak
,
Ender Konukoğlu
,
Anton S. Becker
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1148/ryai.230138
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1148/ryai.230138
Purpose To investigate the accuracy and robustness of prostate segmentation using deep learning across various training data sizes, MRI vendors, prostate zones, and testing methods relative to fellowship-trained diagnostic radiologists. Materials and Methods In this systematic review, Embase, PubMed, Scopus, and Web of Science databases were queried for English-language articles using keywords and related terms for prostate MRI segmentation and deep learning algorithms dated to July 31, 2022. A total of 691 articles from the search query were collected and subsequently filtered to 48 on the basis of predefined inclusion and exclusion criteria. Multiple characteristics were extracted from selected studies, such as deep learning algorithm performance, MRI vendor, and training dataset features. The primary outcome was comparison of mean Dice similarity coefficient (DSC) for prostate segmentation for deep learning algorithms versus diagnostic radiologists. Results Forty-eight studies were included. Most published deep learning algorithms for whole prostate gland segmentation (39 of 42 [93%]) had a DSC at or above expert level (DSC ≥ 0.86). The mean DSC was 0.79 ± 0.06 (SD) for peripheral zone, 0.87 ± 0.05 for transition zone, and 0.90 ± 0.04 for whole prostate gland segmentation. For selected studies that used one major MRI vendor, the mean DSCs of each were as follows: General Electric (three of 48 studies), 0.92 ± 0.03; Philips (four of 48 studies), 0.92 ± 0.02; and Siemens (six of 48 studies), 0.91 ± 0.03. Conclusion Deep learning algorithms for prostate MRI segmentation demonstrated accuracy similar to that of expert radiologists despite varying parameters; therefore, future research should shift toward evaluating segmentation robustness and patient outcomes across diverse clinical settings. Keywords: MRI, Genital/Reproductive, Prostate Segmentation, Deep Learning Systematic review registration link: osf.io/nxaev © RSNA, 2024.
Related Topics
Concepts
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1148/ryai.230138
- OA Status
- green
- Cited By
- 20
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393855384
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4393855384Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1148/ryai.230138Digital Object Identifier
- Title
-
Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic ReviewWork title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-03Full publication date if available
- Authors
-
Mohammad-Kasim Fassia, Adithya Balasubramanian, Sungmin Woo, Hebert Alberto Vargas, Hedvig Hricak, Ender Konukoğlu, Anton S. BeckerList of authors in order
- Landing page
-
https://doi.org/10.1148/ryai.230138Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/11294957Direct OA link when available
- Concepts
-
Artificial intelligence, Deep learning, Computer science, Segmentation, Prostate, Machine learning, Robustness (evolution), Medicine, Medical physics, Internal medicine, Chemistry, Cancer, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
20Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 16, 2024: 4Per-year citation counts (last 5 years)
- References (count)
-
66Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4393855384 |
|---|---|
| doi | https://doi.org/10.1148/ryai.230138 |
| ids.doi | https://doi.org/10.1148/ryai.230138 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/38568094 |
| ids.openalex | https://openalex.org/W4393855384 |
| fwci | 17.25246283 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D006801 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Humans |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D000077321 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Deep Learning |
| mesh[2].qualifier_ui | Q000379 |
| mesh[2].descriptor_ui | D008279 |
| mesh[2].is_major_topic | True |
| mesh[2].qualifier_name | methods |
| mesh[2].descriptor_name | Magnetic Resonance Imaging |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D008297 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Male |
| mesh[4].qualifier_ui | Q000000981 |
| mesh[4].descriptor_ui | D011471 |
| mesh[4].is_major_topic | True |
| mesh[4].qualifier_name | diagnostic imaging |
| mesh[4].descriptor_name | Prostatic Neoplasms |
| mesh[5].qualifier_ui | Q000000981 |
| mesh[5].descriptor_ui | D011467 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | diagnostic imaging |
| mesh[5].descriptor_name | Prostate |
| mesh[6].qualifier_ui | Q000033 |
| mesh[6].descriptor_ui | D011467 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | anatomy & histology |
| mesh[6].descriptor_name | Prostate |
| mesh[7].qualifier_ui | Q000379 |
| mesh[7].descriptor_ui | D007090 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | methods |
| mesh[7].descriptor_name | Image Interpretation, Computer-Assisted |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D006801 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Humans |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D000077321 |
| mesh[9].is_major_topic | True |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Deep Learning |
| mesh[10].qualifier_ui | Q000379 |
| mesh[10].descriptor_ui | D008279 |
| mesh[10].is_major_topic | True |
| mesh[10].qualifier_name | methods |
| mesh[10].descriptor_name | Magnetic Resonance Imaging |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D008297 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Male |
| mesh[12].qualifier_ui | Q000000981 |
| mesh[12].descriptor_ui | D011471 |
| mesh[12].is_major_topic | True |
| mesh[12].qualifier_name | diagnostic imaging |
| mesh[12].descriptor_name | Prostatic Neoplasms |
| mesh[13].qualifier_ui | Q000000981 |
| mesh[13].descriptor_ui | D011467 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | diagnostic imaging |
| mesh[13].descriptor_name | Prostate |
| mesh[14].qualifier_ui | Q000033 |
| mesh[14].descriptor_ui | D011467 |
| mesh[14].is_major_topic | False |
| mesh[14].qualifier_name | anatomy & histology |
| mesh[14].descriptor_name | Prostate |
| mesh[15].qualifier_ui | Q000379 |
| mesh[15].descriptor_ui | D007090 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | methods |
| mesh[15].descriptor_name | Image Interpretation, Computer-Assisted |
| mesh[16].qualifier_ui | |
| mesh[16].descriptor_ui | D006801 |
| mesh[16].is_major_topic | False |
| mesh[16].qualifier_name | |
| mesh[16].descriptor_name | Humans |
| mesh[17].qualifier_ui | |
| mesh[17].descriptor_ui | D000077321 |
| mesh[17].is_major_topic | True |
| mesh[17].qualifier_name | |
| mesh[17].descriptor_name | Deep Learning |
| mesh[18].qualifier_ui | Q000379 |
| mesh[18].descriptor_ui | D008279 |
| mesh[18].is_major_topic | True |
| mesh[18].qualifier_name | methods |
| mesh[18].descriptor_name | Magnetic Resonance Imaging |
| mesh[19].qualifier_ui | |
| mesh[19].descriptor_ui | D008297 |
| mesh[19].is_major_topic | False |
| mesh[19].qualifier_name | |
| mesh[19].descriptor_name | Male |
| mesh[20].qualifier_ui | Q000000981 |
| mesh[20].descriptor_ui | D011471 |
| mesh[20].is_major_topic | True |
| mesh[20].qualifier_name | diagnostic imaging |
| mesh[20].descriptor_name | Prostatic Neoplasms |
| mesh[21].qualifier_ui | Q000000981 |
| mesh[21].descriptor_ui | D011467 |
| mesh[21].is_major_topic | False |
| mesh[21].qualifier_name | diagnostic imaging |
| mesh[21].descriptor_name | Prostate |
| mesh[22].qualifier_ui | Q000033 |
| mesh[22].descriptor_ui | D011467 |
| mesh[22].is_major_topic | False |
| mesh[22].qualifier_name | anatomy & histology |
| mesh[22].descriptor_name | Prostate |
| mesh[23].qualifier_ui | Q000379 |
| mesh[23].descriptor_ui | D007090 |
| mesh[23].is_major_topic | False |
| mesh[23].qualifier_name | methods |
| mesh[23].descriptor_name | Image Interpretation, Computer-Assisted |
| mesh[24].qualifier_ui | |
| mesh[24].descriptor_ui | D006801 |
| mesh[24].is_major_topic | False |
| mesh[24].qualifier_name | |
| mesh[24].descriptor_name | Humans |
| mesh[25].qualifier_ui | |
| mesh[25].descriptor_ui | D000077321 |
| mesh[25].is_major_topic | True |
| mesh[25].qualifier_name | |
| mesh[25].descriptor_name | Deep Learning |
| mesh[26].qualifier_ui | Q000379 |
| mesh[26].descriptor_ui | D008279 |
| mesh[26].is_major_topic | True |
| mesh[26].qualifier_name | methods |
| mesh[26].descriptor_name | Magnetic Resonance Imaging |
| mesh[27].qualifier_ui | |
| mesh[27].descriptor_ui | D008297 |
| mesh[27].is_major_topic | False |
| mesh[27].qualifier_name | |
| mesh[27].descriptor_name | Male |
| mesh[28].qualifier_ui | Q000000981 |
| mesh[28].descriptor_ui | D011471 |
| mesh[28].is_major_topic | True |
| mesh[28].qualifier_name | diagnostic imaging |
| mesh[28].descriptor_name | Prostatic Neoplasms |
| mesh[29].qualifier_ui | Q000000981 |
| mesh[29].descriptor_ui | D011467 |
| mesh[29].is_major_topic | False |
| mesh[29].qualifier_name | diagnostic imaging |
| mesh[29].descriptor_name | Prostate |
| mesh[30].qualifier_ui | Q000033 |
| mesh[30].descriptor_ui | D011467 |
| mesh[30].is_major_topic | False |
| mesh[30].qualifier_name | anatomy & histology |
| mesh[30].descriptor_name | Prostate |
| mesh[31].qualifier_ui | Q000379 |
| mesh[31].descriptor_ui | D007090 |
| mesh[31].is_major_topic | False |
| mesh[31].qualifier_name | methods |
| mesh[31].descriptor_name | Image Interpretation, Computer-Assisted |
| mesh[32].qualifier_ui | |
| mesh[32].descriptor_ui | D006801 |
| mesh[32].is_major_topic | False |
| mesh[32].qualifier_name | |
| mesh[32].descriptor_name | Humans |
| mesh[33].qualifier_ui | |
| mesh[33].descriptor_ui | D000077321 |
| mesh[33].is_major_topic | True |
| mesh[33].qualifier_name | |
| mesh[33].descriptor_name | Deep Learning |
| mesh[34].qualifier_ui | Q000379 |
| mesh[34].descriptor_ui | D008279 |
| mesh[34].is_major_topic | True |
| mesh[34].qualifier_name | methods |
| mesh[34].descriptor_name | Magnetic Resonance Imaging |
| mesh[35].qualifier_ui | |
| mesh[35].descriptor_ui | D008297 |
| mesh[35].is_major_topic | False |
| mesh[35].qualifier_name | |
| mesh[35].descriptor_name | Male |
| mesh[36].qualifier_ui | Q000000981 |
| mesh[36].descriptor_ui | D011471 |
| mesh[36].is_major_topic | True |
| mesh[36].qualifier_name | diagnostic imaging |
| mesh[36].descriptor_name | Prostatic Neoplasms |
| mesh[37].qualifier_ui | Q000000981 |
| mesh[37].descriptor_ui | D011467 |
| mesh[37].is_major_topic | False |
| mesh[37].qualifier_name | diagnostic imaging |
| mesh[37].descriptor_name | Prostate |
| mesh[38].qualifier_ui | Q000033 |
| mesh[38].descriptor_ui | D011467 |
| mesh[38].is_major_topic | False |
| mesh[38].qualifier_name | anatomy & histology |
| mesh[38].descriptor_name | Prostate |
| mesh[39].qualifier_ui | Q000379 |
| mesh[39].descriptor_ui | D007090 |
| mesh[39].is_major_topic | False |
| mesh[39].qualifier_name | methods |
| mesh[39].descriptor_name | Image Interpretation, Computer-Assisted |
| type | review |
| title | Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review |
| biblio.issue | 4 |
| biblio.volume | 6 |
| biblio.last_page | e230138 |
| biblio.first_page | e230138 |
| topics[0].id | https://openalex.org/T10124 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9970999956130981 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2740 |
| topics[0].subfield.display_name | Pulmonary and Respiratory Medicine |
| topics[0].display_name | Prostate Cancer Diagnosis and Treatment |
| topics[1].id | https://openalex.org/T14510 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.984000027179718 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2204 |
| topics[1].subfield.display_name | Biomedical Engineering |
| topics[1].display_name | Medical Imaging and Analysis |
| topics[2].id | https://openalex.org/T13309 |
| topics[2].field.id | https://openalex.org/fields/18 |
| topics[2].field.display_name | Decision Sciences |
| topics[2].score | 0.9388999938964844 |
| topics[2].domain.id | https://openalex.org/domains/2 |
| topics[2].domain.display_name | Social Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1804 |
| topics[2].subfield.display_name | Statistics, Probability and Uncertainty |
| topics[2].display_name | Reliability and Agreement in Measurement |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.6982107162475586 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C108583219 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6335311532020569 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[1].display_name | Deep learning |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6277049779891968 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C89600930 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6249089241027832 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[3].display_name | Segmentation |
| concepts[4].id | https://openalex.org/C2776235491 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5998368263244629 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q9625 |
| concepts[4].display_name | Prostate |
| concepts[5].id | https://openalex.org/C119857082 |
| concepts[5].level | 1 |
| concepts[5].score | 0.5504360198974609 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[5].display_name | Machine learning |
| concepts[6].id | https://openalex.org/C63479239 |
| concepts[6].level | 3 |
| concepts[6].score | 0.5129665732383728 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7353546 |
| concepts[6].display_name | Robustness (evolution) |
| concepts[7].id | https://openalex.org/C71924100 |
| concepts[7].level | 0 |
| concepts[7].score | 0.3939814567565918 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[7].display_name | Medicine |
| concepts[8].id | https://openalex.org/C19527891 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3448466658592224 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1120908 |
| concepts[8].display_name | Medical physics |
| concepts[9].id | https://openalex.org/C126322002 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[9].display_name | Internal medicine |
| concepts[10].id | https://openalex.org/C185592680 |
| concepts[10].level | 0 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[10].display_name | Chemistry |
| concepts[11].id | https://openalex.org/C121608353 |
| concepts[11].level | 2 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q12078 |
| concepts[11].display_name | Cancer |
| concepts[12].id | https://openalex.org/C104317684 |
| concepts[12].level | 2 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[12].display_name | Gene |
| concepts[13].id | https://openalex.org/C55493867 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[13].display_name | Biochemistry |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.6982107162475586 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/deep-learning |
| keywords[1].score | 0.6335311532020569 |
| keywords[1].display_name | Deep learning |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6277049779891968 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/segmentation |
| keywords[3].score | 0.6249089241027832 |
| keywords[3].display_name | Segmentation |
| keywords[4].id | https://openalex.org/keywords/prostate |
| keywords[4].score | 0.5998368263244629 |
| keywords[4].display_name | Prostate |
| keywords[5].id | https://openalex.org/keywords/machine-learning |
| keywords[5].score | 0.5504360198974609 |
| keywords[5].display_name | Machine learning |
| keywords[6].id | https://openalex.org/keywords/robustness |
| keywords[6].score | 0.5129665732383728 |
| keywords[6].display_name | Robustness (evolution) |
| keywords[7].id | https://openalex.org/keywords/medicine |
| keywords[7].score | 0.3939814567565918 |
| keywords[7].display_name | Medicine |
| keywords[8].id | https://openalex.org/keywords/medical-physics |
| keywords[8].score | 0.3448466658592224 |
| keywords[8].display_name | Medical physics |
| language | en |
| locations[0].id | doi:10.1148/ryai.230138 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S4210219523 |
| locations[0].source.issn | 2638-6100 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 2638-6100 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Radiology Artificial Intelligence |
| locations[0].source.host_organization | https://openalex.org/P4310315931 |
| locations[0].source.host_organization_name | Radiological Society of North America |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310315931 |
| locations[0].source.host_organization_lineage_names | Radiological Society of North America |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Radiology: Artificial Intelligence |
| locations[0].landing_page_url | https://doi.org/10.1148/ryai.230138 |
| locations[1].id | pmid:38568094 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Radiology. Artificial intelligence |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/38568094 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:11294957 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Radiol Artif Intell |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11294957 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5093280760 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Mohammad-Kasim Fassia |
| authorships[0].countries | CH, US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I1334819555, https://openalex.org/I205783295, https://openalex.org/I35440088 |
| authorships[0].affiliations[0].raw_affiliation_string | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| authorships[0].institutions[0].id | https://openalex.org/I35440088 |
| authorships[0].institutions[0].ror | https://ror.org/05a28rw58 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I2799323385, https://openalex.org/I35440088 |
| authorships[0].institutions[0].country_code | CH |
| authorships[0].institutions[0].display_name | ETH Zurich |
| authorships[0].institutions[1].id | https://openalex.org/I205783295 |
| authorships[0].institutions[1].ror | https://ror.org/05bnh6r87 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I205783295 |
| authorships[0].institutions[1].country_code | US |
| authorships[0].institutions[1].display_name | Cornell University |
| authorships[0].institutions[2].id | https://openalex.org/I1334819555 |
| authorships[0].institutions[2].ror | https://ror.org/02yrq0923 |
| authorships[0].institutions[2].type | healthcare |
| authorships[0].institutions[2].lineage | https://openalex.org/I1334819555 |
| authorships[0].institutions[2].country_code | US |
| authorships[0].institutions[2].display_name | Memorial Sloan Kettering Cancer Center |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Mohammad-Kasim Fassia |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| authorships[1].author.id | https://openalex.org/A5041738492 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-7912-8896 |
| authorships[1].author.display_name | Adithya Balasubramanian |
| authorships[1].countries | CH, US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I1334819555, https://openalex.org/I205783295, https://openalex.org/I35440088 |
| authorships[1].affiliations[0].raw_affiliation_string | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| authorships[1].institutions[0].id | https://openalex.org/I35440088 |
| authorships[1].institutions[0].ror | https://ror.org/05a28rw58 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I2799323385, https://openalex.org/I35440088 |
| authorships[1].institutions[0].country_code | CH |
| authorships[1].institutions[0].display_name | ETH Zurich |
| authorships[1].institutions[1].id | https://openalex.org/I205783295 |
| authorships[1].institutions[1].ror | https://ror.org/05bnh6r87 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I205783295 |
| authorships[1].institutions[1].country_code | US |
| authorships[1].institutions[1].display_name | Cornell University |
| authorships[1].institutions[2].id | https://openalex.org/I1334819555 |
| authorships[1].institutions[2].ror | https://ror.org/02yrq0923 |
| authorships[1].institutions[2].type | healthcare |
| authorships[1].institutions[2].lineage | https://openalex.org/I1334819555 |
| authorships[1].institutions[2].country_code | US |
| authorships[1].institutions[2].display_name | Memorial Sloan Kettering Cancer Center |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Adithya Balasubramanian |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| authorships[2].author.id | https://openalex.org/A5024328581 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-8459-8369 |
| authorships[2].author.display_name | Sungmin Woo |
| authorships[2].countries | CH, US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I1334819555, https://openalex.org/I205783295, https://openalex.org/I35440088 |
| authorships[2].affiliations[0].raw_affiliation_string | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| authorships[2].institutions[0].id | https://openalex.org/I35440088 |
| authorships[2].institutions[0].ror | https://ror.org/05a28rw58 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I2799323385, https://openalex.org/I35440088 |
| authorships[2].institutions[0].country_code | CH |
| authorships[2].institutions[0].display_name | ETH Zurich |
| authorships[2].institutions[1].id | https://openalex.org/I205783295 |
| authorships[2].institutions[1].ror | https://ror.org/05bnh6r87 |
| authorships[2].institutions[1].type | education |
| authorships[2].institutions[1].lineage | https://openalex.org/I205783295 |
| authorships[2].institutions[1].country_code | US |
| authorships[2].institutions[1].display_name | Cornell University |
| authorships[2].institutions[2].id | https://openalex.org/I1334819555 |
| authorships[2].institutions[2].ror | https://ror.org/02yrq0923 |
| authorships[2].institutions[2].type | healthcare |
| authorships[2].institutions[2].lineage | https://openalex.org/I1334819555 |
| authorships[2].institutions[2].country_code | US |
| authorships[2].institutions[2].display_name | Memorial Sloan Kettering Cancer Center |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Sungmin Woo |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| authorships[3].author.id | https://openalex.org/A5002973886 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-1169-133X |
| authorships[3].author.display_name | Hebert Alberto Vargas |
| authorships[3].countries | CH, US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I1334819555, https://openalex.org/I205783295, https://openalex.org/I35440088 |
| authorships[3].affiliations[0].raw_affiliation_string | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| authorships[3].institutions[0].id | https://openalex.org/I35440088 |
| authorships[3].institutions[0].ror | https://ror.org/05a28rw58 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I2799323385, https://openalex.org/I35440088 |
| authorships[3].institutions[0].country_code | CH |
| authorships[3].institutions[0].display_name | ETH Zurich |
| authorships[3].institutions[1].id | https://openalex.org/I205783295 |
| authorships[3].institutions[1].ror | https://ror.org/05bnh6r87 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I205783295 |
| authorships[3].institutions[1].country_code | US |
| authorships[3].institutions[1].display_name | Cornell University |
| authorships[3].institutions[2].id | https://openalex.org/I1334819555 |
| authorships[3].institutions[2].ror | https://ror.org/02yrq0923 |
| authorships[3].institutions[2].type | healthcare |
| authorships[3].institutions[2].lineage | https://openalex.org/I1334819555 |
| authorships[3].institutions[2].country_code | US |
| authorships[3].institutions[2].display_name | Memorial Sloan Kettering Cancer Center |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Hebert Alberto Vargas |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| authorships[4].author.id | https://openalex.org/A5021013894 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-2240-9694 |
| authorships[4].author.display_name | Hedvig Hricak |
| authorships[4].countries | CH, US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I1334819555, https://openalex.org/I205783295, https://openalex.org/I35440088 |
| authorships[4].affiliations[0].raw_affiliation_string | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| authorships[4].institutions[0].id | https://openalex.org/I35440088 |
| authorships[4].institutions[0].ror | https://ror.org/05a28rw58 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I2799323385, https://openalex.org/I35440088 |
| authorships[4].institutions[0].country_code | CH |
| authorships[4].institutions[0].display_name | ETH Zurich |
| authorships[4].institutions[1].id | https://openalex.org/I205783295 |
| authorships[4].institutions[1].ror | https://ror.org/05bnh6r87 |
| authorships[4].institutions[1].type | education |
| authorships[4].institutions[1].lineage | https://openalex.org/I205783295 |
| authorships[4].institutions[1].country_code | US |
| authorships[4].institutions[1].display_name | Cornell University |
| authorships[4].institutions[2].id | https://openalex.org/I1334819555 |
| authorships[4].institutions[2].ror | https://ror.org/02yrq0923 |
| authorships[4].institutions[2].type | healthcare |
| authorships[4].institutions[2].lineage | https://openalex.org/I1334819555 |
| authorships[4].institutions[2].country_code | US |
| authorships[4].institutions[2].display_name | Memorial Sloan Kettering Cancer Center |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Hedvig Hricak |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| authorships[5].author.id | https://openalex.org/A5036970822 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-2542-3611 |
| authorships[5].author.display_name | Ender Konukoğlu |
| authorships[5].countries | CH, US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I1334819555, https://openalex.org/I205783295, https://openalex.org/I35440088 |
| authorships[5].affiliations[0].raw_affiliation_string | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| authorships[5].institutions[0].id | https://openalex.org/I35440088 |
| authorships[5].institutions[0].ror | https://ror.org/05a28rw58 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I2799323385, https://openalex.org/I35440088 |
| authorships[5].institutions[0].country_code | CH |
| authorships[5].institutions[0].display_name | ETH Zurich |
| authorships[5].institutions[1].id | https://openalex.org/I205783295 |
| authorships[5].institutions[1].ror | https://ror.org/05bnh6r87 |
| authorships[5].institutions[1].type | education |
| authorships[5].institutions[1].lineage | https://openalex.org/I205783295 |
| authorships[5].institutions[1].country_code | US |
| authorships[5].institutions[1].display_name | Cornell University |
| authorships[5].institutions[2].id | https://openalex.org/I1334819555 |
| authorships[5].institutions[2].ror | https://ror.org/02yrq0923 |
| authorships[5].institutions[2].type | healthcare |
| authorships[5].institutions[2].lineage | https://openalex.org/I1334819555 |
| authorships[5].institutions[2].country_code | US |
| authorships[5].institutions[2].display_name | Memorial Sloan Kettering Cancer Center |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Ender Konukoglu |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| authorships[6].author.id | https://openalex.org/A5011964941 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-8372-6496 |
| authorships[6].author.display_name | Anton S. Becker |
| authorships[6].countries | CH, US |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I1334819555, https://openalex.org/I205783295, https://openalex.org/I35440088 |
| authorships[6].affiliations[0].raw_affiliation_string | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| authorships[6].institutions[0].id | https://openalex.org/I35440088 |
| authorships[6].institutions[0].ror | https://ror.org/05a28rw58 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I2799323385, https://openalex.org/I35440088 |
| authorships[6].institutions[0].country_code | CH |
| authorships[6].institutions[0].display_name | ETH Zurich |
| authorships[6].institutions[1].id | https://openalex.org/I205783295 |
| authorships[6].institutions[1].ror | https://ror.org/05bnh6r87 |
| authorships[6].institutions[1].type | education |
| authorships[6].institutions[1].lineage | https://openalex.org/I205783295 |
| authorships[6].institutions[1].country_code | US |
| authorships[6].institutions[1].display_name | Cornell University |
| authorships[6].institutions[2].id | https://openalex.org/I1334819555 |
| authorships[6].institutions[2].ror | https://ror.org/02yrq0923 |
| authorships[6].institutions[2].type | healthcare |
| authorships[6].institutions[2].lineage | https://openalex.org/I1334819555 |
| authorships[6].institutions[2].country_code | US |
| authorships[6].institutions[2].display_name | Memorial Sloan Kettering Cancer Center |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Anton S. Becker |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.). |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11294957 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10124 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9970999956130981 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2740 |
| primary_topic.subfield.display_name | Pulmonary and Respiratory Medicine |
| primary_topic.display_name | Prostate Cancer Diagnosis and Treatment |
| related_works | https://openalex.org/W4375867731, https://openalex.org/W2770593030, https://openalex.org/W2611989081, https://openalex.org/W3154990682, https://openalex.org/W2560201613, https://openalex.org/W2171975302, https://openalex.org/W2022352247, https://openalex.org/W4230611425, https://openalex.org/W2731899572, https://openalex.org/W2377538627 |
| cited_by_count | 20 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 16 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 4 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:pubmedcentral.nih.gov:11294957 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764455111 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | PubMed Central |
| best_oa_location.source.host_organization | https://openalex.org/I1299303238 |
| best_oa_location.source.host_organization_name | National Institutes of Health |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | Radiol Artif Intell |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11294957 |
| primary_location.id | doi:10.1148/ryai.230138 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S4210219523 |
| primary_location.source.issn | 2638-6100 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2638-6100 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Radiology Artificial Intelligence |
| primary_location.source.host_organization | https://openalex.org/P4310315931 |
| primary_location.source.host_organization_name | Radiological Society of North America |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310315931 |
| primary_location.source.host_organization_lineage_names | Radiological Society of North America |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Radiology: Artificial Intelligence |
| primary_location.landing_page_url | https://doi.org/10.1148/ryai.230138 |
| publication_date | 2024-04-03 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2073117151, https://openalex.org/W3049053440, https://openalex.org/W2900516813, https://openalex.org/W3017249016, https://openalex.org/W2005501262, https://openalex.org/W2323200062, https://openalex.org/W2982215855, https://openalex.org/W3165584499, https://openalex.org/W4386023995, https://openalex.org/W4322503359, https://openalex.org/W2942231644, https://openalex.org/W3168641556, https://openalex.org/W3111857857, https://openalex.org/W3173250677, https://openalex.org/W2889360161, https://openalex.org/W2124823491, https://openalex.org/W3102100346, https://openalex.org/W3174112297, https://openalex.org/W3016181437, https://openalex.org/W3159396227, https://openalex.org/W3122868258, https://openalex.org/W3131993672, https://openalex.org/W3036855771, https://openalex.org/W2969515110, https://openalex.org/W2972429586, https://openalex.org/W2922165539, https://openalex.org/W2958620810, https://openalex.org/W2762363380, https://openalex.org/W4210495299, https://openalex.org/W3112600384, https://openalex.org/W2803522971, https://openalex.org/W3033221365, https://openalex.org/W3012587841, https://openalex.org/W4200127817, https://openalex.org/W3019362006, https://openalex.org/W3071727253, https://openalex.org/W3004531689, https://openalex.org/W2988053426, https://openalex.org/W3089094239, https://openalex.org/W3093860814, https://openalex.org/W4205708468, https://openalex.org/W2964334073, https://openalex.org/W3093421122, https://openalex.org/W3184298112, https://openalex.org/W4280493273, https://openalex.org/W3200107768, https://openalex.org/W3034265573, https://openalex.org/W2887533690, https://openalex.org/W3051093578, https://openalex.org/W3176421227, https://openalex.org/W2914806156, https://openalex.org/W2907769417, https://openalex.org/W2939917862, https://openalex.org/W3013376562, https://openalex.org/W2915725179, https://openalex.org/W2896001621, https://openalex.org/W2790296028, https://openalex.org/W3099805905, https://openalex.org/W3131517655, https://openalex.org/W3100708836, https://openalex.org/W2766076071, https://openalex.org/W2748151168, https://openalex.org/W2915606070, https://openalex.org/W2792466349, https://openalex.org/W2951505611, https://openalex.org/W3164731060 |
| referenced_works_count | 66 |
| abstract_inverted_index.A | 68 |
| abstract_inverted_index.a | 153 |
| abstract_inverted_index.42 | 150 |
| abstract_inverted_index.48 | 83, 210, 218, 227 |
| abstract_inverted_index.In | 33 |
| abstract_inverted_index.To | 1 |
| abstract_inverted_index.as | 101, 204 |
| abstract_inverted_index.at | 155 |
| abstract_inverted_index.of | 7, 42, 70, 87, 117, 149, 201, 209, 217, 226, 245 |
| abstract_inverted_index.on | 84 |
| abstract_inverted_index.or | 156 |
| abstract_inverted_index.to | 26, 64, 82, 243 |
| abstract_inverted_index.© | 279 |
| abstract_inverted_index.± | 168, 175, 182, 213, 221, 230 |
| abstract_inverted_index.(39 | 148 |
| abstract_inverted_index.31, | 66 |
| abstract_inverted_index.691 | 71 |
| abstract_inverted_index.DSC | 154, 165 |
| abstract_inverted_index.For | 189 |
| abstract_inverted_index.MRI | 18, 57, 106, 196, 238 |
| abstract_inverted_index.The | 112, 163 |
| abstract_inverted_index.Web | 41 |
| abstract_inverted_index.and | 5, 22, 31, 40, 52, 59, 79, 90, 108, 180, 223, 260 |
| abstract_inverted_index.for | 47, 55, 123, 126, 143, 171, 177, 184, 236 |
| abstract_inverted_index.had | 152 |
| abstract_inverted_index.one | 194 |
| abstract_inverted_index.the | 3, 74, 85, 198 |
| abstract_inverted_index.was | 115, 166 |
| abstract_inverted_index.≥ | 161 |
| abstract_inverted_index.(DSC | 160 |
| abstract_inverted_index.(SD) | 170 |
| abstract_inverted_index.(six | 225 |
| abstract_inverted_index.0.04 | 183 |
| abstract_inverted_index.0.05 | 176 |
| abstract_inverted_index.0.06 | 169 |
| abstract_inverted_index.0.79 | 167 |
| abstract_inverted_index.0.87 | 174 |
| abstract_inverted_index.0.90 | 181 |
| abstract_inverted_index.0.91 | 229 |
| abstract_inverted_index.0.92 | 212, 220 |
| abstract_inverted_index.DSCs | 200 |
| abstract_inverted_index.Deep | 233, 272 |
| abstract_inverted_index.Dice | 119 |
| abstract_inverted_index.July | 65 |
| abstract_inverted_index.MRI, | 268 |
| abstract_inverted_index.Most | 138 |
| abstract_inverted_index.data | 16 |
| abstract_inverted_index.deep | 11, 60, 102, 127, 140 |
| abstract_inverted_index.each | 202 |
| abstract_inverted_index.from | 73, 97 |
| abstract_inverted_index.mean | 118, 164, 199 |
| abstract_inverted_index.such | 100 |
| abstract_inverted_index.that | 192, 244 |
| abstract_inverted_index.this | 34 |
| abstract_inverted_index.used | 193 |
| abstract_inverted_index.were | 45, 77, 95, 136, 203 |
| abstract_inverted_index.(DSC) | 122 |
| abstract_inverted_index.(four | 216 |
| abstract_inverted_index.0.02; | 222 |
| abstract_inverted_index.0.03. | 231 |
| abstract_inverted_index.0.03; | 214 |
| abstract_inverted_index.2022. | 67 |
| abstract_inverted_index.2024. | 281 |
| abstract_inverted_index.RSNA, | 280 |
| abstract_inverted_index.above | 157 |
| abstract_inverted_index.basis | 86 |
| abstract_inverted_index.dated | 63 |
| abstract_inverted_index.gland | 146, 187 |
| abstract_inverted_index.level | 159 |
| abstract_inverted_index.link: | 277 |
| abstract_inverted_index.major | 195 |
| abstract_inverted_index.query | 76 |
| abstract_inverted_index.shift | 255 |
| abstract_inverted_index.terms | 54 |
| abstract_inverted_index.total | 69 |
| abstract_inverted_index.using | 10, 50 |
| abstract_inverted_index.whole | 144, 185 |
| abstract_inverted_index.zone, | 173, 179 |
| abstract_inverted_index.(three | 208 |
| abstract_inverted_index.0.86). | 162 |
| abstract_inverted_index.[93%]) | 151 |
| abstract_inverted_index.across | 13, 263 |
| abstract_inverted_index.expert | 158, 246 |
| abstract_inverted_index.future | 252 |
| abstract_inverted_index.review | 275 |
| abstract_inverted_index.search | 75 |
| abstract_inverted_index.should | 254 |
| abstract_inverted_index.sizes, | 17 |
| abstract_inverted_index.toward | 256 |
| abstract_inverted_index.versus | 130 |
| abstract_inverted_index.zones, | 21 |
| abstract_inverted_index.Embase, | 37 |
| abstract_inverted_index.General | 206 |
| abstract_inverted_index.Methods | 32 |
| abstract_inverted_index.Philips | 215 |
| abstract_inverted_index.PubMed, | 38 |
| abstract_inverted_index.Purpose | 0 |
| abstract_inverted_index.Results | 133 |
| abstract_inverted_index.Science | 43 |
| abstract_inverted_index.Scopus, | 39 |
| abstract_inverted_index.Siemens | 224 |
| abstract_inverted_index.dataset | 110 |
| abstract_inverted_index.despite | 248 |
| abstract_inverted_index.diverse | 264 |
| abstract_inverted_index.methods | 24 |
| abstract_inverted_index.outcome | 114 |
| abstract_inverted_index.patient | 261 |
| abstract_inverted_index.primary | 113 |
| abstract_inverted_index.queried | 46 |
| abstract_inverted_index.related | 53 |
| abstract_inverted_index.review, | 36 |
| abstract_inverted_index.similar | 242 |
| abstract_inverted_index.studies | 135, 191 |
| abstract_inverted_index.testing | 23 |
| abstract_inverted_index.various | 14 |
| abstract_inverted_index.varying | 249 |
| abstract_inverted_index.vendor, | 107, 197 |
| abstract_inverted_index.Electric | 207 |
| abstract_inverted_index.Learning | 273 |
| abstract_inverted_index.Multiple | 93 |
| abstract_inverted_index.Prostate | 270 |
| abstract_inverted_index.accuracy | 4, 241 |
| abstract_inverted_index.articles | 49, 72 |
| abstract_inverted_index.clinical | 265 |
| abstract_inverted_index.filtered | 81 |
| abstract_inverted_index.follows: | 205 |
| abstract_inverted_index.keywords | 51 |
| abstract_inverted_index.learning | 12, 61, 103, 128, 141, 234 |
| abstract_inverted_index.outcomes | 262 |
| abstract_inverted_index.prostate | 8, 20, 56, 124, 145, 186, 237 |
| abstract_inverted_index.relative | 25 |
| abstract_inverted_index.research | 253 |
| abstract_inverted_index.selected | 98, 190 |
| abstract_inverted_index.studies, | 99 |
| abstract_inverted_index.training | 15, 109 |
| abstract_inverted_index.vendors, | 19 |
| abstract_inverted_index.Materials | 30 |
| abstract_inverted_index.algorithm | 104 |
| abstract_inverted_index.collected | 78 |
| abstract_inverted_index.criteria. | 92 |
| abstract_inverted_index.databases | 44 |
| abstract_inverted_index.exclusion | 91 |
| abstract_inverted_index.extracted | 96 |
| abstract_inverted_index.features. | 111 |
| abstract_inverted_index.included. | 137 |
| abstract_inverted_index.inclusion | 89 |
| abstract_inverted_index.published | 139 |
| abstract_inverted_index.settings. | 266 |
| abstract_inverted_index.studies), | 211, 219, 228 |
| abstract_inverted_index.Conclusion | 232 |
| abstract_inverted_index.Systematic | 274 |
| abstract_inverted_index.algorithms | 62, 129, 142, 235 |
| abstract_inverted_index.comparison | 116 |
| abstract_inverted_index.diagnostic | 28, 131 |
| abstract_inverted_index.evaluating | 257 |
| abstract_inverted_index.peripheral | 172 |
| abstract_inverted_index.predefined | 88 |
| abstract_inverted_index.robustness | 6, 259 |
| abstract_inverted_index.similarity | 120 |
| abstract_inverted_index.systematic | 35 |
| abstract_inverted_index.therefore, | 251 |
| abstract_inverted_index.transition | 178 |
| abstract_inverted_index.Forty-eight | 134 |
| abstract_inverted_index.coefficient | 121 |
| abstract_inverted_index.investigate | 2 |
| abstract_inverted_index.parameters; | 250 |
| abstract_inverted_index.demonstrated | 240 |
| abstract_inverted_index.osf.io/nxaev | 278 |
| abstract_inverted_index.performance, | 105 |
| abstract_inverted_index.radiologists | 247 |
| abstract_inverted_index.registration | 276 |
| abstract_inverted_index.segmentation | 9, 58, 125, 147, 239, 258 |
| abstract_inverted_index.subsequently | 80 |
| abstract_inverted_index.Segmentation, | 271 |
| abstract_inverted_index.radiologists. | 29, 132 |
| abstract_inverted_index.segmentation. | 188 |
| abstract_inverted_index.characteristics | 94 |
| abstract_inverted_index.<b>Keywords:</b> | 267 |
| abstract_inverted_index.English-language | 48 |
| abstract_inverted_index.fellowship-trained | 27 |
| abstract_inverted_index.Genital/Reproductive, | 269 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.7400000095367432 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.9885583 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |