Whole‐body tumor segmentation from PET/CT images using a two‐stage cascaded neural network with camouflaged object detection mechanisms Article Swipe
Jiangping He
,
Yanjie Zhang
,
Maggie Chung
,
M. Wang
,
Kun Wang
,
Yan Ma
,
Xiaoyang Ding
,
Qiang Li
,
Yonglin Pu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/mp.16438
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/mp.16438
Background Whole‐body Metabolic Tumor Volume (MTVwb) is an independent prognostic factor for overall survival in lung cancer patients. Automatic segmentation methods have been proposed for MTV calculation. Nevertheless, most of existing methods for patients with lung cancer only segment tumors in the thoracic region. Purpose In this paper, we present a Two‐Stage cascaded neural network integrated with Camouflaged Object Detection mEchanisms (TS‐Code‐Net) for automatic segmenting tumors from whole‐body PET/CT images. Methods Firstly, tumors are detected from the Maximum Intensity Projection (MIP) images of PET/CT scans, and tumors' approximate localizations along z ‐axis are identified. Secondly, the segmentations are performed on PET/CT slices that contain tumors identified by the first step. Camouflaged object detection mechanisms are utilized to distinguish the tumors from their surrounding regions that have similar Standard Uptake Values (SUV) and texture appearance. Finally, the TS‐Code‐Net is trained by minimizing the total loss that incorporates the segmentation accuracy loss and the class imbalance loss. Results The performance of the TS‐Code‐Net is tested on a whole‐body PET/CT image data‐set including 480 Non‐Small Cell Lung Cancer (NSCLC) patients with five‐fold cross‐validation using image segmentation metrics. Our method achieves 0.70, 0.76, and 0.70, for Dice, Sensitivity and Precision, respectively, which demonstrates the superiority of the TS‐Code‐Net over several existing methods related to metastatic lung cancer segmentation from whole‐body PET/CT images. Conclusions The proposed TS‐Code‐Net is effective for whole‐body tumor segmentation of PET/CT images. Codes for TS‐Code‐Net are available at: https://github.com/zyj19/TS‐Code‐Net .
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/mp.16438
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.16438
- OA Status
- bronze
- Cited By
- 9
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4367835341
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4367835341Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/mp.16438Digital Object Identifier
- Title
-
Whole‐body tumor segmentation from PET/CT images using a two‐stage cascaded neural network with camouflaged object detection mechanismsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-03Full publication date if available
- Authors
-
Jiangping He, Yanjie Zhang, Maggie Chung, M. Wang, Kun Wang, Yan Ma, Xiaoyang Ding, Qiang Li, Yonglin PuList of authors in order
- Landing page
-
https://doi.org/10.1002/mp.16438Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.16438Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.16438Direct OA link when available
- Concepts
-
Segmentation, Lung cancer, Artificial intelligence, Computer science, Image segmentation, Stage (stratigraphy), Nuclear medicine, Pattern recognition (psychology), Medicine, Pathology, Biology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 2, 2023: 3Per-year citation counts (last 5 years)
- References (count)
-
51Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4367835341 |
|---|---|
| doi | https://doi.org/10.1002/mp.16438 |
| ids.doi | https://doi.org/10.1002/mp.16438 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/37134002 |
| ids.openalex | https://openalex.org/W4367835341 |
| fwci | 2.78149469 |
| 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 | Q000379 |
| mesh[1].descriptor_ui | D000072078 |
| mesh[1].is_major_topic | False |
| mesh[1].qualifier_name | methods |
| mesh[1].descriptor_name | Positron Emission Tomography Computed Tomography |
| mesh[2].qualifier_ui | Q000473 |
| mesh[2].descriptor_ui | D002289 |
| mesh[2].is_major_topic | True |
| mesh[2].qualifier_name | pathology |
| mesh[2].descriptor_name | Carcinoma, Non-Small-Cell Lung |
| mesh[3].qualifier_ui | Q000473 |
| mesh[3].descriptor_ui | D008175 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | pathology |
| mesh[3].descriptor_name | Lung Neoplasms |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D016571 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Neural Networks, Computer |
| mesh[5].qualifier_ui | Q000473 |
| mesh[5].descriptor_ui | D060726 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | pathology |
| mesh[5].descriptor_name | Torso |
| mesh[6].qualifier_ui | Q000379 |
| mesh[6].descriptor_ui | D007091 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | methods |
| mesh[6].descriptor_name | Image Processing, Computer-Assisted |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D006801 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Humans |
| mesh[8].qualifier_ui | Q000379 |
| mesh[8].descriptor_ui | D000072078 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | methods |
| mesh[8].descriptor_name | Positron Emission Tomography Computed Tomography |
| mesh[9].qualifier_ui | Q000473 |
| mesh[9].descriptor_ui | D002289 |
| mesh[9].is_major_topic | True |
| mesh[9].qualifier_name | pathology |
| mesh[9].descriptor_name | Carcinoma, Non-Small-Cell Lung |
| mesh[10].qualifier_ui | Q000473 |
| mesh[10].descriptor_ui | D008175 |
| mesh[10].is_major_topic | True |
| mesh[10].qualifier_name | pathology |
| mesh[10].descriptor_name | Lung Neoplasms |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D016571 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Neural Networks, Computer |
| mesh[12].qualifier_ui | Q000473 |
| mesh[12].descriptor_ui | D060726 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | pathology |
| mesh[12].descriptor_name | Torso |
| mesh[13].qualifier_ui | Q000379 |
| mesh[13].descriptor_ui | D007091 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | methods |
| mesh[13].descriptor_name | Image Processing, Computer-Assisted |
| type | article |
| title | Whole‐body tumor segmentation from PET/CT images using a two‐stage cascaded neural network with camouflaged object detection mechanisms |
| biblio.issue | 10 |
| biblio.volume | 50 |
| biblio.last_page | 6162 |
| biblio.first_page | 6151 |
| topics[0].id | https://openalex.org/T12422 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9997000098228455 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2741 |
| topics[0].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[0].display_name | Radiomics and Machine Learning in Medical Imaging |
| topics[1].id | https://openalex.org/T10202 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9955000281333923 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2740 |
| topics[1].subfield.display_name | Pulmonary and Respiratory Medicine |
| topics[1].display_name | Lung Cancer Diagnosis and Treatment |
| topics[2].id | https://openalex.org/T10158 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9896000027656555 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2730 |
| topics[2].subfield.display_name | Oncology |
| topics[2].display_name | Cancer Immunotherapy and Biomarkers |
| is_xpac | False |
| apc_list.value | 3040 |
| apc_list.currency | USD |
| apc_list.value_usd | 3040 |
| apc_paid | |
| concepts[0].id | https://openalex.org/C89600930 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7324748635292053 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[0].display_name | Segmentation |
| concepts[1].id | https://openalex.org/C2776256026 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5369099974632263 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q47912 |
| concepts[1].display_name | Lung cancer |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5341666340827942 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.5147898197174072 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C124504099 |
| concepts[4].level | 3 |
| concepts[4].score | 0.4739035665988922 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q56933 |
| concepts[4].display_name | Image segmentation |
| concepts[5].id | https://openalex.org/C146357865 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4213985800743103 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1123245 |
| concepts[5].display_name | Stage (stratigraphy) |
| concepts[6].id | https://openalex.org/C2989005 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4026724100112915 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q214963 |
| concepts[6].display_name | Nuclear medicine |
| concepts[7].id | https://openalex.org/C153180895 |
| concepts[7].level | 2 |
| concepts[7].score | 0.3817947506904602 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[7].display_name | Pattern recognition (psychology) |
| concepts[8].id | https://openalex.org/C71924100 |
| concepts[8].level | 0 |
| concepts[8].score | 0.2812436521053314 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[8].display_name | Medicine |
| concepts[9].id | https://openalex.org/C142724271 |
| concepts[9].level | 1 |
| concepts[9].score | 0.1418120265007019 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7208 |
| concepts[9].display_name | Pathology |
| concepts[10].id | https://openalex.org/C86803240 |
| concepts[10].level | 0 |
| concepts[10].score | 0.09673130512237549 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[10].display_name | Biology |
| concepts[11].id | https://openalex.org/C151730666 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[11].display_name | Paleontology |
| keywords[0].id | https://openalex.org/keywords/segmentation |
| keywords[0].score | 0.7324748635292053 |
| keywords[0].display_name | Segmentation |
| keywords[1].id | https://openalex.org/keywords/lung-cancer |
| keywords[1].score | 0.5369099974632263 |
| keywords[1].display_name | Lung cancer |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.5341666340827942 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.5147898197174072 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/image-segmentation |
| keywords[4].score | 0.4739035665988922 |
| keywords[4].display_name | Image segmentation |
| keywords[5].id | https://openalex.org/keywords/stage |
| keywords[5].score | 0.4213985800743103 |
| keywords[5].display_name | Stage (stratigraphy) |
| keywords[6].id | https://openalex.org/keywords/nuclear-medicine |
| keywords[6].score | 0.4026724100112915 |
| keywords[6].display_name | Nuclear medicine |
| keywords[7].id | https://openalex.org/keywords/pattern-recognition |
| keywords[7].score | 0.3817947506904602 |
| keywords[7].display_name | Pattern recognition (psychology) |
| keywords[8].id | https://openalex.org/keywords/medicine |
| keywords[8].score | 0.2812436521053314 |
| keywords[8].display_name | Medicine |
| keywords[9].id | https://openalex.org/keywords/pathology |
| keywords[9].score | 0.1418120265007019 |
| keywords[9].display_name | Pathology |
| keywords[10].id | https://openalex.org/keywords/biology |
| keywords[10].score | 0.09673130512237549 |
| keywords[10].display_name | Biology |
| language | en |
| locations[0].id | doi:10.1002/mp.16438 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S95522064 |
| locations[0].source.issn | 0094-2405, 1522-8541, 2473-4209 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0094-2405 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Medical Physics |
| locations[0].source.host_organization | https://openalex.org/P4310320595 |
| locations[0].source.host_organization_name | Wiley |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320595 |
| locations[0].source.host_organization_lineage_names | Wiley |
| locations[0].license | |
| locations[0].pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.16438 |
| 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 | Medical Physics |
| locations[0].landing_page_url | https://doi.org/10.1002/mp.16438 |
| locations[1].id | pmid:37134002 |
| 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 | Medical physics |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/37134002 |
| locations[2].id | pmh:oai:uchicago.tind.io:5862 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306402460 |
| 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 | Knowledge@UChicago (University of Chicago) |
| locations[2].source.host_organization | https://openalex.org/I40347166 |
| locations[2].source.host_organization_name | University of Chicago |
| locations[2].source.host_organization_lineage | https://openalex.org/I40347166 |
| locations[2].license | cc-by-nc-nd |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | http://knowledge.uchicago.edu/record/5862 |
| locations[2].landing_page_url | http://knowledge.uchicago.edu/record/5862 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5101012041 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Jiangping He |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210152533 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210152533 |
| authorships[0].institutions[0].ror | https://ror.org/04v7yv031 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210152533 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Lanzhou University of Finance and Economics |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jiangping He |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China |
| authorships[1].author.id | https://openalex.org/A5088933473 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-7964-2326 |
| authorships[1].author.display_name | Yanjie Zhang |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210152533 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China |
| authorships[1].institutions[0].id | https://openalex.org/I4210152533 |
| authorships[1].institutions[0].ror | https://ror.org/04v7yv031 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210152533 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Lanzhou University of Finance and Economics |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yangjie Zhang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China |
| authorships[2].author.id | https://openalex.org/A5028281094 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-8144-381X |
| authorships[2].author.display_name | Maggie Chung |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I180670191 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Radiology, University of California, San Francisco, California, USA |
| authorships[2].institutions[0].id | https://openalex.org/I180670191 |
| authorships[2].institutions[0].ror | https://ror.org/043mz5j54 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I180670191 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | University of California, San Francisco |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Maggie Chung |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Radiology, University of California, San Francisco, California, USA |
| authorships[3].author.id | https://openalex.org/A5101694733 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-4713-9646 |
| authorships[3].author.display_name | M. Wang |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I180670191 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Pathology, University of California, San Francisco, California, USA |
| authorships[3].institutions[0].id | https://openalex.org/I180670191 |
| authorships[3].institutions[0].ror | https://ror.org/043mz5j54 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I180670191 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | University of California, San Francisco |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Michael Wang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Pathology, University of California, San Francisco, California, USA |
| authorships[4].author.id | https://openalex.org/A5100366717 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-2513-768X |
| authorships[4].author.display_name | Kun Wang |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210152533 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China |
| authorships[4].institutions[0].id | https://openalex.org/I4210152533 |
| authorships[4].institutions[0].ror | https://ror.org/04v7yv031 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210152533 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Lanzhou University of Finance and Economics |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Kun Wang |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China |
| authorships[5].author.id | https://openalex.org/A5115595718 |
| authorships[5].author.orcid | https://orcid.org/0009-0004-7895-0863 |
| authorships[5].author.display_name | Yan Ma |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I4210152533 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China |
| authorships[5].institutions[0].id | https://openalex.org/I4210152533 |
| authorships[5].institutions[0].ror | https://ror.org/04v7yv031 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I4210152533 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Lanzhou University of Finance and Economics |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Yan Ma |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China |
| authorships[6].author.id | https://openalex.org/A5068900468 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Xiaoyang Ding |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I4210152533 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China |
| authorships[6].institutions[0].id | https://openalex.org/I4210152533 |
| authorships[6].institutions[0].ror | https://ror.org/04v7yv031 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I4210152533 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Lanzhou University of Finance and Economics |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Xiaoyang Ding |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China |
| authorships[7].author.id | https://openalex.org/A5069420468 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-7309-2220 |
| authorships[7].author.display_name | Qiang Li |
| authorships[7].countries | CN |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I4210152533 |
| authorships[7].affiliations[0].raw_affiliation_string | Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China |
| authorships[7].institutions[0].id | https://openalex.org/I4210152533 |
| authorships[7].institutions[0].ror | https://ror.org/04v7yv031 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I4210152533 |
| authorships[7].institutions[0].country_code | CN |
| authorships[7].institutions[0].display_name | Lanzhou University of Finance and Economics |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Qiang Li |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China |
| authorships[8].author.id | https://openalex.org/A5064824079 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-9583-8185 |
| authorships[8].author.display_name | Yonglin Pu |
| authorships[8].countries | US |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I40347166 |
| authorships[8].affiliations[0].raw_affiliation_string | Department of Radiology, University of Chicago, Chicago, Illinois, USA |
| authorships[8].institutions[0].id | https://openalex.org/I40347166 |
| authorships[8].institutions[0].ror | https://ror.org/024mw5h28 |
| authorships[8].institutions[0].type | education |
| authorships[8].institutions[0].lineage | https://openalex.org/I40347166 |
| authorships[8].institutions[0].country_code | US |
| authorships[8].institutions[0].display_name | University of Chicago |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Yonglin Pu |
| authorships[8].is_corresponding | True |
| authorships[8].raw_affiliation_strings | Department of Radiology, University of Chicago, Chicago, Illinois, USA |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.16438 |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Whole‐body tumor segmentation from PET/CT images using a two‐stage cascaded neural network with camouflaged object detection mechanisms |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12422 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9997000098228455 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2741 |
| primary_topic.subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| primary_topic.display_name | Radiomics and Machine Learning in Medical Imaging |
| related_works | https://openalex.org/W2503350049, https://openalex.org/W2329386257, https://openalex.org/W2005585032, https://openalex.org/W2397616145, https://openalex.org/W2064573700, https://openalex.org/W1988477118, https://openalex.org/W2554284703, https://openalex.org/W4324058133, https://openalex.org/W2397320258, https://openalex.org/W1522196789 |
| cited_by_count | 9 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 4 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 3 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1002/mp.16438 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S95522064 |
| best_oa_location.source.issn | 0094-2405, 1522-8541, 2473-4209 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0094-2405 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Medical Physics |
| best_oa_location.source.host_organization | https://openalex.org/P4310320595 |
| best_oa_location.source.host_organization_name | Wiley |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320595 |
| best_oa_location.source.host_organization_lineage_names | Wiley |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.16438 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Medical Physics |
| best_oa_location.landing_page_url | https://doi.org/10.1002/mp.16438 |
| primary_location.id | doi:10.1002/mp.16438 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S95522064 |
| primary_location.source.issn | 0094-2405, 1522-8541, 2473-4209 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0094-2405 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Medical Physics |
| primary_location.source.host_organization | https://openalex.org/P4310320595 |
| primary_location.source.host_organization_name | Wiley |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320595 |
| primary_location.source.host_organization_lineage_names | Wiley |
| primary_location.license | |
| primary_location.pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.16438 |
| 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 | Medical Physics |
| primary_location.landing_page_url | https://doi.org/10.1002/mp.16438 |
| publication_date | 2023-05-03 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3128646645, https://openalex.org/W3119005666, https://openalex.org/W2100285775, https://openalex.org/W2037109555, https://openalex.org/W2019929699, https://openalex.org/W2169769956, https://openalex.org/W2140783866, https://openalex.org/W2054516549, https://openalex.org/W2080593651, https://openalex.org/W748803803, https://openalex.org/W2119173284, https://openalex.org/W2582769321, https://openalex.org/W2963446712, https://openalex.org/W6725739302, https://openalex.org/W2565639579, https://openalex.org/W3151922143, https://openalex.org/W1536680647, https://openalex.org/W4247924304, https://openalex.org/W2194775991, https://openalex.org/W2928165649, https://openalex.org/W4200066891, https://openalex.org/W4224220441, https://openalex.org/W2972183494, https://openalex.org/W2979777575, https://openalex.org/W2790493168, https://openalex.org/W2898441006, https://openalex.org/W2782993895, https://openalex.org/W2432481613, https://openalex.org/W2962914239, https://openalex.org/W1901129140, https://openalex.org/W3093550081, https://openalex.org/W4210381648, https://openalex.org/W2905101266, https://openalex.org/W2941458854, https://openalex.org/W2895328943, https://openalex.org/W3130171175, https://openalex.org/W1892880783, https://openalex.org/W3034684132, https://openalex.org/W3155043039, https://openalex.org/W3168112135, https://openalex.org/W3183430956, https://openalex.org/W2769040523, https://openalex.org/W1622676895, https://openalex.org/W2913447510, https://openalex.org/W6682889407, https://openalex.org/W2900237898, https://openalex.org/W3096579235, https://openalex.org/W2963881378, https://openalex.org/W2156387975, https://openalex.org/W3106286734, https://openalex.org/W2963604034 |
| referenced_works_count | 51 |
| abstract_inverted_index.. | 239 |
| abstract_inverted_index.a | 51, 165 |
| abstract_inverted_index.z | 91 |
| abstract_inverted_index.In | 46 |
| abstract_inverted_index.an | 8 |
| abstract_inverted_index.by | 107, 140 |
| abstract_inverted_index.in | 15, 41 |
| abstract_inverted_index.is | 7, 138, 162, 223 |
| abstract_inverted_index.of | 30, 83, 159, 202, 229 |
| abstract_inverted_index.on | 100, 164 |
| abstract_inverted_index.to | 117, 210 |
| abstract_inverted_index.we | 49 |
| abstract_inverted_index.480 | 171 |
| abstract_inverted_index.MTV | 26 |
| abstract_inverted_index.Our | 185 |
| abstract_inverted_index.The | 157, 220 |
| abstract_inverted_index.and | 86, 132, 151, 190, 195 |
| abstract_inverted_index.are | 74, 93, 98, 115, 235 |
| abstract_inverted_index.at: | 237 |
| abstract_inverted_index.for | 12, 25, 33, 63, 192, 225, 233 |
| abstract_inverted_index.the | 42, 77, 96, 108, 119, 136, 142, 147, 152, 160, 200, 203 |
| abstract_inverted_index.Cell | 173 |
| abstract_inverted_index.Lung | 174 |
| abstract_inverted_index.been | 23 |
| abstract_inverted_index.from | 67, 76, 121, 215 |
| abstract_inverted_index.have | 22, 126 |
| abstract_inverted_index.loss | 144, 150 |
| abstract_inverted_index.lung | 16, 36, 212 |
| abstract_inverted_index.most | 29 |
| abstract_inverted_index.only | 38 |
| abstract_inverted_index.over | 205 |
| abstract_inverted_index.that | 103, 125, 145 |
| abstract_inverted_index.this | 47 |
| abstract_inverted_index.with | 35, 57, 178 |
| abstract_inverted_index.(MIP) | 81 |
| abstract_inverted_index.(SUV) | 131 |
| abstract_inverted_index.0.70, | 188, 191 |
| abstract_inverted_index.0.76, | 189 |
| abstract_inverted_index.Codes | 232 |
| abstract_inverted_index.Dice, | 193 |
| abstract_inverted_index.Tumor | 4 |
| abstract_inverted_index.along | 90 |
| abstract_inverted_index.class | 153 |
| abstract_inverted_index.first | 109 |
| abstract_inverted_index.image | 168, 182 |
| abstract_inverted_index.loss. | 155 |
| abstract_inverted_index.step. | 110 |
| abstract_inverted_index.their | 122 |
| abstract_inverted_index.total | 143 |
| abstract_inverted_index.tumor | 227 |
| abstract_inverted_index.using | 181 |
| abstract_inverted_index.which | 198 |
| abstract_inverted_index.Cancer | 175 |
| abstract_inverted_index.Object | 59 |
| abstract_inverted_index.PET/CT | 69, 84, 101, 167, 217, 230 |
| abstract_inverted_index.Uptake | 129 |
| abstract_inverted_index.Values | 130 |
| abstract_inverted_index.Volume | 5 |
| abstract_inverted_index.cancer | 17, 37, 213 |
| abstract_inverted_index.factor | 11 |
| abstract_inverted_index.images | 82 |
| abstract_inverted_index.method | 186 |
| abstract_inverted_index.neural | 54 |
| abstract_inverted_index.object | 112 |
| abstract_inverted_index.paper, | 48 |
| abstract_inverted_index.scans, | 85 |
| abstract_inverted_index.slices | 102 |
| abstract_inverted_index.tested | 163 |
| abstract_inverted_index.tumors | 40, 66, 73, 105, 120 |
| abstract_inverted_index.(MTVwb) | 6 |
| abstract_inverted_index.(NSCLC) | 176 |
| abstract_inverted_index.Maximum | 78 |
| abstract_inverted_index.Methods | 71 |
| abstract_inverted_index.Purpose | 45 |
| abstract_inverted_index.Results | 156 |
| abstract_inverted_index.contain | 104 |
| abstract_inverted_index.images. | 70, 218, 231 |
| abstract_inverted_index.methods | 21, 32, 208 |
| abstract_inverted_index.network | 55 |
| abstract_inverted_index.overall | 13 |
| abstract_inverted_index.present | 50 |
| abstract_inverted_index.region. | 44 |
| abstract_inverted_index.regions | 124 |
| abstract_inverted_index.related | 209 |
| abstract_inverted_index.segment | 39 |
| abstract_inverted_index.several | 206 |
| abstract_inverted_index.similar | 127 |
| abstract_inverted_index.texture | 133 |
| abstract_inverted_index.trained | 139 |
| abstract_inverted_index.tumors' | 87 |
| abstract_inverted_index.‐axis | 92 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Finally, | 135 |
| abstract_inverted_index.Firstly, | 72 |
| abstract_inverted_index.Standard | 128 |
| abstract_inverted_index.accuracy | 149 |
| abstract_inverted_index.achieves | 187 |
| abstract_inverted_index.cascaded | 53 |
| abstract_inverted_index.detected | 75 |
| abstract_inverted_index.existing | 31, 207 |
| abstract_inverted_index.metrics. | 184 |
| abstract_inverted_index.patients | 34, 177 |
| abstract_inverted_index.proposed | 24, 221 |
| abstract_inverted_index.survival | 14 |
| abstract_inverted_index.thoracic | 43 |
| abstract_inverted_index.utilized | 116 |
| abstract_inverted_index.Automatic | 19 |
| abstract_inverted_index.Detection | 60 |
| abstract_inverted_index.Intensity | 79 |
| abstract_inverted_index.Metabolic | 3 |
| abstract_inverted_index.Secondly, | 95 |
| abstract_inverted_index.automatic | 64 |
| abstract_inverted_index.available | 236 |
| abstract_inverted_index.detection | 113 |
| abstract_inverted_index.effective | 224 |
| abstract_inverted_index.imbalance | 154 |
| abstract_inverted_index.including | 170 |
| abstract_inverted_index.patients. | 18 |
| abstract_inverted_index.performed | 99 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Precision, | 196 |
| abstract_inverted_index.Projection | 80 |
| abstract_inverted_index.data‐set | 169 |
| abstract_inverted_index.identified | 106 |
| abstract_inverted_index.integrated | 56 |
| abstract_inverted_index.mEchanisms | 61 |
| abstract_inverted_index.mechanisms | 114 |
| abstract_inverted_index.metastatic | 211 |
| abstract_inverted_index.minimizing | 141 |
| abstract_inverted_index.prognostic | 10 |
| abstract_inverted_index.segmenting | 65 |
| abstract_inverted_index.Camouflaged | 58, 111 |
| abstract_inverted_index.Conclusions | 219 |
| abstract_inverted_index.Non‐Small | 172 |
| abstract_inverted_index.Sensitivity | 194 |
| abstract_inverted_index.Two‐Stage | 52 |
| abstract_inverted_index.appearance. | 134 |
| abstract_inverted_index.approximate | 88 |
| abstract_inverted_index.distinguish | 118 |
| abstract_inverted_index.five‐fold | 179 |
| abstract_inverted_index.identified. | 94 |
| abstract_inverted_index.independent | 9 |
| abstract_inverted_index.performance | 158 |
| abstract_inverted_index.superiority | 201 |
| abstract_inverted_index.surrounding | 123 |
| abstract_inverted_index.Whole‐body | 2 |
| abstract_inverted_index.calculation. | 27 |
| abstract_inverted_index.demonstrates | 199 |
| abstract_inverted_index.incorporates | 146 |
| abstract_inverted_index.segmentation | 20, 148, 183, 214, 228 |
| abstract_inverted_index.whole‐body | 68, 166, 216, 226 |
| abstract_inverted_index.Nevertheless, | 28 |
| abstract_inverted_index.localizations | 89 |
| abstract_inverted_index.respectively, | 197 |
| abstract_inverted_index.segmentations | 97 |
| abstract_inverted_index.TS‐Code‐Net | 137, 161, 204, 222, 234 |
| abstract_inverted_index.(TS‐Code‐Net) | 62 |
| abstract_inverted_index.cross‐validation | 180 |
| abstract_inverted_index.https://github.com/zyj19/TS‐Code‐Net | 238 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5064824079 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I40347166 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8399999737739563 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.88673673 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |