IMG-22. A DEEP LEARNING MODEL FOR AUTOMATIC POSTERIOR FOSSA PEDIATRIC BRAIN TUMOR SEGMENTATION: A MULTI-INSTITUTIONAL STUDY Article Swipe
Lydia Tam
,
Edward Lee
,
Michelle Han
,
Jason N. Wright
,
Yi Chen
,
Jenn Quon
,
Robert M. Lober
,
Tina Young Poussaint
,
Gerald A. Grant
,
Michael D. Taylor
,
Vijay Ramaswamy
,
Chang Yueh Ho
,
Samuel Cheshier
,
Mourad Ben Saïd
,
Nick Vitanza
,
Michael S. B. Edwards
,
Kristen W. Yeom
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1093/neuonc/noaa222.357
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1093/neuonc/noaa222.357
BACKGROUND Brain tumors are the most common solid malignancies in childhood, many of which develop in the posterior fossa (PF). Manual tumor measurements are frequently required to optimize registration into surgical navigation systems or for surveillance of nonresectable tumors after therapy. With recent advances in artificial intelligence (AI), automated MRI-based tumor segmentation is now feasible without requiring manual measurements. Our goal was to create a deep learning model for automated PF tumor segmentation that can register into navigation systems and provide volume output. METHODS 720 pre-surgical MRI scans from five pediatric centers were divided into training, validation, and testing datasets. The study cohort comprised of four PF tumor types: medulloblastoma, diffuse midline glioma, ependymoma, and brainstem or cerebellar pilocytic astrocytoma. Manual segmentation of the tumors by an attending neuroradiologist served as “ground truth” labels for model training and evaluation. We used 2D Unet, an encoder-decoder convolutional neural network architecture, with a pre-trained ResNet50 encoder. We assessed ventricle segmentation accuracy on a held-out test set using Dice similarity coefficient (0–1) and compared ventricular volume calculation between manual and model-derived segmentations using linear regression. RESULTS Compared to the ground truth expert human segmentation, overall Dice score for model performance accuracy was 0.83 for automatic delineation of the 4 tumor types. CONCLUSIONS In this multi-institutional study, we present a deep learning algorithm that automatically delineates PF tumors and outputs volumetric information. Our results demonstrate applied AI that is clinically applicable, potentially augmenting radiologists, neuro-oncologists, and neurosurgeons for tumor evaluation, surveillance, and surgical planning.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/neuonc/noaa222.357
- https://academic.oup.com/neuro-oncology/article-pdf/22/Supplement_3/iii359/34687167/noaa222.357.pdf
- OA Status
- hybrid
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3111988545
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3111988545Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/neuonc/noaa222.357Digital Object Identifier
- Title
-
IMG-22. A DEEP LEARNING MODEL FOR AUTOMATIC POSTERIOR FOSSA PEDIATRIC BRAIN TUMOR SEGMENTATION: A MULTI-INSTITUTIONAL STUDYWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-12-01Full publication date if available
- Authors
-
Lydia Tam, Edward Lee, Michelle Han, Jason N. Wright, Yi Chen, Jenn Quon, Robert M. Lober, Tina Young Poussaint, Gerald A. Grant, Michael D. Taylor, Vijay Ramaswamy, Chang Yueh Ho, Samuel Cheshier, Mourad Ben Saïd, Nick Vitanza, Michael S. B. Edwards, Kristen W. YeomList of authors in order
- Landing page
-
https://doi.org/10.1093/neuonc/noaa222.357Publisher landing page
- PDF URL
-
https://academic.oup.com/neuro-oncology/article-pdf/22/Supplement_3/iii359/34687167/noaa222.357.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/neuro-oncology/article-pdf/22/Supplement_3/iii359/34687167/noaa222.357.pdfDirect OA link when available
- Concepts
-
Segmentation, Deep learning, Computer science, Ground truth, Artificial intelligence, Medulloblastoma, Ependymoma, Pilocytic astrocytoma, Convolutional neural network, Neuroradiologist, Medicine, Magnetic resonance imaging, Radiology, Astrocytoma, Glioma, Pathology, Cancer researchTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3111988545 |
|---|---|
| doi | https://doi.org/10.1093/neuonc/noaa222.357 |
| ids.doi | https://doi.org/10.1093/neuonc/noaa222.357 |
| ids.mag | 3111988545 |
| ids.openalex | https://openalex.org/W3111988545 |
| fwci | 0.0 |
| type | article |
| title | IMG-22. A DEEP LEARNING MODEL FOR AUTOMATIC POSTERIOR FOSSA PEDIATRIC BRAIN TUMOR SEGMENTATION: A MULTI-INSTITUTIONAL STUDY |
| biblio.issue | Supplement_3 |
| biblio.volume | 22 |
| biblio.last_page | iii359 |
| biblio.first_page | iii359 |
| topics[0].id | https://openalex.org/T14510 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9539999961853027 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2204 |
| topics[0].subfield.display_name | Biomedical Engineering |
| topics[0].display_name | Medical Imaging and Analysis |
| topics[1].id | https://openalex.org/T10129 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9539999961853027 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2716 |
| topics[1].subfield.display_name | Genetics |
| topics[1].display_name | Glioma Diagnosis and Treatment |
| topics[2].id | https://openalex.org/T12702 |
| topics[2].field.id | https://openalex.org/fields/28 |
| topics[2].field.display_name | Neuroscience |
| topics[2].score | 0.9462000131607056 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2808 |
| topics[2].subfield.display_name | Neurology |
| topics[2].display_name | Brain Tumor Detection and Classification |
| is_xpac | False |
| apc_list.value | 4612 |
| apc_list.currency | USD |
| apc_list.value_usd | 4612 |
| apc_paid.value | 4612 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 4612 |
| concepts[0].id | https://openalex.org/C89600930 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6869726777076721 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[0].display_name | Segmentation |
| concepts[1].id | https://openalex.org/C108583219 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6699784994125366 |
| 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 | 0.6504941582679749 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C146849305 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6245514154434204 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q370766 |
| concepts[3].display_name | Ground truth |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.6236372590065002 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C2780789225 |
| concepts[5].level | 2 |
| concepts[5].score | 0.567652702331543 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1333608 |
| concepts[5].display_name | Medulloblastoma |
| concepts[6].id | https://openalex.org/C2781432949 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5562343001365662 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1346753 |
| concepts[6].display_name | Ependymoma |
| concepts[7].id | https://openalex.org/C2777722931 |
| concepts[7].level | 4 |
| concepts[7].score | 0.5353012681007385 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2095252 |
| concepts[7].display_name | Pilocytic astrocytoma |
| concepts[8].id | https://openalex.org/C81363708 |
| concepts[8].level | 2 |
| concepts[8].score | 0.5108386874198914 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[8].display_name | Convolutional neural network |
| concepts[9].id | https://openalex.org/C2909330790 |
| concepts[9].level | 3 |
| concepts[9].score | 0.47187891602516174 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q642836 |
| concepts[9].display_name | Neuroradiologist |
| concepts[10].id | https://openalex.org/C71924100 |
| concepts[10].level | 0 |
| concepts[10].score | 0.3632742166519165 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[10].display_name | Medicine |
| concepts[11].id | https://openalex.org/C143409427 |
| concepts[11].level | 2 |
| concepts[11].score | 0.30657488107681274 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q161238 |
| concepts[11].display_name | Magnetic resonance imaging |
| concepts[12].id | https://openalex.org/C126838900 |
| concepts[12].level | 1 |
| concepts[12].score | 0.3035924732685089 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q77604 |
| concepts[12].display_name | Radiology |
| concepts[13].id | https://openalex.org/C2778880634 |
| concepts[13].level | 3 |
| concepts[13].score | 0.25780266523361206 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q177755 |
| concepts[13].display_name | Astrocytoma |
| concepts[14].id | https://openalex.org/C2778227246 |
| concepts[14].level | 2 |
| concepts[14].score | 0.21296530961990356 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1365309 |
| concepts[14].display_name | Glioma |
| concepts[15].id | https://openalex.org/C142724271 |
| concepts[15].level | 1 |
| concepts[15].score | 0.11509066820144653 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7208 |
| concepts[15].display_name | Pathology |
| concepts[16].id | https://openalex.org/C502942594 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q3421914 |
| concepts[16].display_name | Cancer research |
| keywords[0].id | https://openalex.org/keywords/segmentation |
| keywords[0].score | 0.6869726777076721 |
| keywords[0].display_name | Segmentation |
| keywords[1].id | https://openalex.org/keywords/deep-learning |
| keywords[1].score | 0.6699784994125366 |
| keywords[1].display_name | Deep learning |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6504941582679749 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/ground-truth |
| keywords[3].score | 0.6245514154434204 |
| keywords[3].display_name | Ground truth |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.6236372590065002 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/medulloblastoma |
| keywords[5].score | 0.567652702331543 |
| keywords[5].display_name | Medulloblastoma |
| keywords[6].id | https://openalex.org/keywords/ependymoma |
| keywords[6].score | 0.5562343001365662 |
| keywords[6].display_name | Ependymoma |
| keywords[7].id | https://openalex.org/keywords/pilocytic-astrocytoma |
| keywords[7].score | 0.5353012681007385 |
| keywords[7].display_name | Pilocytic astrocytoma |
| keywords[8].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[8].score | 0.5108386874198914 |
| keywords[8].display_name | Convolutional neural network |
| keywords[9].id | https://openalex.org/keywords/neuroradiologist |
| keywords[9].score | 0.47187891602516174 |
| keywords[9].display_name | Neuroradiologist |
| keywords[10].id | https://openalex.org/keywords/medicine |
| keywords[10].score | 0.3632742166519165 |
| keywords[10].display_name | Medicine |
| keywords[11].id | https://openalex.org/keywords/magnetic-resonance-imaging |
| keywords[11].score | 0.30657488107681274 |
| keywords[11].display_name | Magnetic resonance imaging |
| keywords[12].id | https://openalex.org/keywords/radiology |
| keywords[12].score | 0.3035924732685089 |
| keywords[12].display_name | Radiology |
| keywords[13].id | https://openalex.org/keywords/astrocytoma |
| keywords[13].score | 0.25780266523361206 |
| keywords[13].display_name | Astrocytoma |
| keywords[14].id | https://openalex.org/keywords/glioma |
| keywords[14].score | 0.21296530961990356 |
| keywords[14].display_name | Glioma |
| keywords[15].id | https://openalex.org/keywords/pathology |
| keywords[15].score | 0.11509066820144653 |
| keywords[15].display_name | Pathology |
| language | en |
| locations[0].id | doi:10.1093/neuonc/noaa222.357 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S106908163 |
| locations[0].source.issn | 1522-8517, 1523-5866 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1522-8517 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Neuro-Oncology |
| locations[0].source.host_organization | https://openalex.org/P4310311648 |
| locations[0].source.host_organization_name | Oxford University Press |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310311648 |
| locations[0].license | cc-by-nc |
| locations[0].pdf_url | https://academic.oup.com/neuro-oncology/article-pdf/22/Supplement_3/iii359/34687167/noaa222.357.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Neuro-Oncology |
| locations[0].landing_page_url | https://doi.org/10.1093/neuonc/noaa222.357 |
| locations[1].id | pmh:oai:pubmedcentral.nih.gov:7715226 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S2764455111 |
| 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 Central |
| 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 | submittedVersion |
| locations[1].raw_type | Text |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Neuro Oncol |
| locations[1].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/7715226 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5051294315 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9900-3249 |
| authorships[0].author.display_name | Lydia Tam |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I97018004 |
| authorships[0].affiliations[0].raw_affiliation_string | Stanford University, Stanford, CA, USA |
| authorships[0].institutions[0].id | https://openalex.org/I97018004 |
| authorships[0].institutions[0].ror | https://ror.org/00f54p054 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I97018004 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Stanford University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Lydia Tam |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Stanford University, Stanford, CA, USA |
| authorships[1].author.id | https://openalex.org/A5100670406 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-0418-1454 |
| authorships[1].author.display_name | Edward Lee |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I97018004 |
| authorships[1].affiliations[0].raw_affiliation_string | Stanford University, Stanford, CA, USA |
| authorships[1].institutions[0].id | https://openalex.org/I97018004 |
| authorships[1].institutions[0].ror | https://ror.org/00f54p054 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I97018004 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Stanford University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Edward Lee |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Stanford University, Stanford, CA, USA |
| authorships[2].author.id | https://openalex.org/A5011795641 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-2812-6193 |
| authorships[2].author.display_name | Michelle Han |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I97018004 |
| authorships[2].affiliations[0].raw_affiliation_string | Stanford University, Stanford, CA, USA |
| authorships[2].institutions[0].id | https://openalex.org/I97018004 |
| authorships[2].institutions[0].ror | https://ror.org/00f54p054 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I97018004 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Stanford University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Michelle Han |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Stanford University, Stanford, CA, USA |
| authorships[3].author.id | https://openalex.org/A5060271842 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6676-8253 |
| authorships[3].author.display_name | Jason N. Wright |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I1295499534 |
| authorships[3].affiliations[0].raw_affiliation_string | Seattle Children’s Hospital, Seattle, WA, USA |
| authorships[3].institutions[0].id | https://openalex.org/I1295499534 |
| authorships[3].institutions[0].ror | https://ror.org/01njes783 |
| authorships[3].institutions[0].type | healthcare |
| authorships[3].institutions[0].lineage | https://openalex.org/I1295499534 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Seattle Children's Hospital |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Jason Wright |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Seattle Children’s Hospital, Seattle, WA, USA |
| authorships[4].author.id | https://openalex.org/A5002783173 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-7960-8374 |
| authorships[4].author.display_name | Yi Chen |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I97018004 |
| authorships[4].affiliations[0].raw_affiliation_string | Stanford University, Stanford, CA, USA |
| authorships[4].institutions[0].id | https://openalex.org/I97018004 |
| authorships[4].institutions[0].ror | https://ror.org/00f54p054 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I97018004 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | Stanford University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Leo Chen |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Stanford University, Stanford, CA, USA |
| authorships[5].author.id | https://openalex.org/A5029654494 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Jenn Quon |
| authorships[5].countries | US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I97018004 |
| authorships[5].affiliations[0].raw_affiliation_string | Stanford University, Stanford, CA, USA |
| authorships[5].institutions[0].id | https://openalex.org/I97018004 |
| authorships[5].institutions[0].ror | https://ror.org/00f54p054 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I97018004 |
| authorships[5].institutions[0].country_code | US |
| authorships[5].institutions[0].display_name | Stanford University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Jenn Quon |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Stanford University, Stanford, CA, USA |
| authorships[6].author.id | https://openalex.org/A5036336480 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-0824-0274 |
| authorships[6].author.display_name | Robert M. Lober |
| authorships[6].countries | US |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I4210120983 |
| authorships[6].affiliations[0].raw_affiliation_string | Dayton Children’s Hospital, Dayton, OH, USA |
| authorships[6].institutions[0].id | https://openalex.org/I4210120983 |
| authorships[6].institutions[0].ror | https://ror.org/02wgt3820 |
| authorships[6].institutions[0].type | healthcare |
| authorships[6].institutions[0].lineage | https://openalex.org/I4210120983 |
| authorships[6].institutions[0].country_code | US |
| authorships[6].institutions[0].display_name | Dayton Children's Hospital |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Robert Lober |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Dayton Children’s Hospital, Dayton, OH, USA |
| authorships[7].author.id | https://openalex.org/A5001841105 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-0814-0791 |
| authorships[7].author.display_name | Tina Young Poussaint |
| authorships[7].countries | US |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I1288882113 |
| authorships[7].affiliations[0].raw_affiliation_string | Boston Children’s Hospital, Boston, MA, USA |
| authorships[7].institutions[0].id | https://openalex.org/I1288882113 |
| authorships[7].institutions[0].ror | https://ror.org/00dvg7y05 |
| authorships[7].institutions[0].type | healthcare |
| authorships[7].institutions[0].lineage | https://openalex.org/I1288882113 |
| authorships[7].institutions[0].country_code | US |
| authorships[7].institutions[0].display_name | Boston Children's Hospital |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Tina Poussaint |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Boston Children’s Hospital, Boston, MA, USA |
| authorships[8].author.id | https://openalex.org/A5012203120 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-2651-4603 |
| authorships[8].author.display_name | Gerald A. Grant |
| authorships[8].countries | US |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I97018004 |
| authorships[8].affiliations[0].raw_affiliation_string | Stanford University, Stanford, CA, USA |
| authorships[8].institutions[0].id | https://openalex.org/I97018004 |
| authorships[8].institutions[0].ror | https://ror.org/00f54p054 |
| authorships[8].institutions[0].type | education |
| authorships[8].institutions[0].lineage | https://openalex.org/I97018004 |
| authorships[8].institutions[0].country_code | US |
| authorships[8].institutions[0].display_name | Stanford University |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Gerald Grant |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Stanford University, Stanford, CA, USA |
| authorships[9].author.id | https://openalex.org/A5100644772 |
| authorships[9].author.orcid | https://orcid.org/0000-0001-7009-3466 |
| authorships[9].author.display_name | Michael D. Taylor |
| authorships[9].countries | CA |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I2801317318 |
| authorships[9].affiliations[0].raw_affiliation_string | Hospital for Sick Children, Toronto, ON, Canada |
| authorships[9].institutions[0].id | https://openalex.org/I2801317318 |
| authorships[9].institutions[0].ror | https://ror.org/057q4rt57 |
| authorships[9].institutions[0].type | healthcare |
| authorships[9].institutions[0].lineage | https://openalex.org/I2801317318 |
| authorships[9].institutions[0].country_code | CA |
| authorships[9].institutions[0].display_name | Hospital for Sick Children |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Michael Taylor |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | Hospital for Sick Children, Toronto, ON, Canada |
| authorships[10].author.id | https://openalex.org/A5040595262 |
| authorships[10].author.orcid | https://orcid.org/0000-0002-6557-895X |
| authorships[10].author.display_name | Vijay Ramaswamy |
| authorships[10].countries | CA |
| authorships[10].affiliations[0].institution_ids | https://openalex.org/I2801317318 |
| authorships[10].affiliations[0].raw_affiliation_string | Hospital for Sick Children, Toronto, ON, Canada |
| authorships[10].institutions[0].id | https://openalex.org/I2801317318 |
| authorships[10].institutions[0].ror | https://ror.org/057q4rt57 |
| authorships[10].institutions[0].type | healthcare |
| authorships[10].institutions[0].lineage | https://openalex.org/I2801317318 |
| authorships[10].institutions[0].country_code | CA |
| authorships[10].institutions[0].display_name | Hospital for Sick Children |
| authorships[10].author_position | middle |
| authorships[10].raw_author_name | Vijay Ramaswamy |
| authorships[10].is_corresponding | False |
| authorships[10].raw_affiliation_strings | Hospital for Sick Children, Toronto, ON, Canada |
| authorships[11].author.id | https://openalex.org/A5088602823 |
| authorships[11].author.orcid | https://orcid.org/0000-0002-2358-1851 |
| authorships[11].author.display_name | Chang Yueh Ho |
| authorships[11].countries | US |
| authorships[11].affiliations[0].institution_ids | https://openalex.org/I4394709130, https://openalex.org/I55769427 |
| authorships[11].affiliations[0].raw_affiliation_string | Indiana University School of Medicine, Indianapolis, IN, USA |
| authorships[11].institutions[0].id | https://openalex.org/I4394709130 |
| authorships[11].institutions[0].ror | https://ror.org/02ets8c94 |
| authorships[11].institutions[0].type | education |
| authorships[11].institutions[0].lineage | https://openalex.org/I4394709130, https://openalex.org/I592451 |
| authorships[11].institutions[0].country_code | |
| authorships[11].institutions[0].display_name | Indiana University School of Medicine |
| authorships[11].institutions[1].id | https://openalex.org/I55769427 |
| authorships[11].institutions[1].ror | https://ror.org/05gxnyn08 |
| authorships[11].institutions[1].type | education |
| authorships[11].institutions[1].lineage | https://openalex.org/I55769427, https://openalex.org/I592451 |
| authorships[11].institutions[1].country_code | US |
| authorships[11].institutions[1].display_name | Indiana University – Purdue University Indianapolis |
| authorships[11].author_position | middle |
| authorships[11].raw_author_name | Chang Ho |
| authorships[11].is_corresponding | False |
| authorships[11].raw_affiliation_strings | Indiana University School of Medicine, Indianapolis, IN, USA |
| authorships[12].author.id | https://openalex.org/A5052972832 |
| authorships[12].author.orcid | https://orcid.org/0000-0003-4983-6610 |
| authorships[12].author.display_name | Samuel Cheshier |
| authorships[12].countries | US |
| authorships[12].affiliations[0].institution_ids | https://openalex.org/I223532165 |
| authorships[12].affiliations[0].raw_affiliation_string | University of Utah, Salt Lake City, UT, USA |
| authorships[12].institutions[0].id | https://openalex.org/I223532165 |
| authorships[12].institutions[0].ror | https://ror.org/03r0ha626 |
| authorships[12].institutions[0].type | education |
| authorships[12].institutions[0].lineage | https://openalex.org/I223532165 |
| authorships[12].institutions[0].country_code | US |
| authorships[12].institutions[0].display_name | University of Utah |
| authorships[12].author_position | middle |
| authorships[12].raw_author_name | Samuel Cheshier |
| authorships[12].is_corresponding | False |
| authorships[12].raw_affiliation_strings | University of Utah, Salt Lake City, UT, USA |
| authorships[13].author.id | https://openalex.org/A5075598280 |
| authorships[13].author.orcid | https://orcid.org/0000-0001-5567-4721 |
| authorships[13].author.display_name | Mourad Ben Saïd |
| authorships[13].affiliations[0].raw_affiliation_string | Centre International Carthage Médicale, Monastir, Tunisia |
| authorships[13].author_position | middle |
| authorships[13].raw_author_name | Mourad Said |
| authorships[13].is_corresponding | False |
| authorships[13].raw_affiliation_strings | Centre International Carthage Médicale, Monastir, Tunisia |
| authorships[14].author.id | https://openalex.org/A5040213867 |
| authorships[14].author.orcid | |
| authorships[14].author.display_name | Nick Vitanza |
| authorships[14].countries | US |
| authorships[14].affiliations[0].institution_ids | https://openalex.org/I1295499534 |
| authorships[14].affiliations[0].raw_affiliation_string | Seattle Children’s Hospital, Seattle, WA, USA |
| authorships[14].institutions[0].id | https://openalex.org/I1295499534 |
| authorships[14].institutions[0].ror | https://ror.org/01njes783 |
| authorships[14].institutions[0].type | healthcare |
| authorships[14].institutions[0].lineage | https://openalex.org/I1295499534 |
| authorships[14].institutions[0].country_code | US |
| authorships[14].institutions[0].display_name | Seattle Children's Hospital |
| authorships[14].author_position | middle |
| authorships[14].raw_author_name | Nick Vitanza |
| authorships[14].is_corresponding | False |
| authorships[14].raw_affiliation_strings | Seattle Children’s Hospital, Seattle, WA, USA |
| authorships[15].author.id | https://openalex.org/A5007194357 |
| authorships[15].author.orcid | https://orcid.org/0000-0001-9591-1242 |
| authorships[15].author.display_name | Michael S. B. Edwards |
| authorships[15].countries | US |
| authorships[15].affiliations[0].institution_ids | https://openalex.org/I97018004 |
| authorships[15].affiliations[0].raw_affiliation_string | Stanford University, Stanford, CA, USA |
| authorships[15].institutions[0].id | https://openalex.org/I97018004 |
| authorships[15].institutions[0].ror | https://ror.org/00f54p054 |
| authorships[15].institutions[0].type | education |
| authorships[15].institutions[0].lineage | https://openalex.org/I97018004 |
| authorships[15].institutions[0].country_code | US |
| authorships[15].institutions[0].display_name | Stanford University |
| authorships[15].author_position | middle |
| authorships[15].raw_author_name | Michael Edwards |
| authorships[15].is_corresponding | False |
| authorships[15].raw_affiliation_strings | Stanford University, Stanford, CA, USA |
| authorships[16].author.id | https://openalex.org/A5011081136 |
| authorships[16].author.orcid | https://orcid.org/0000-0001-9860-3368 |
| authorships[16].author.display_name | Kristen W. Yeom |
| authorships[16].countries | US |
| authorships[16].affiliations[0].institution_ids | https://openalex.org/I97018004 |
| authorships[16].affiliations[0].raw_affiliation_string | Stanford University, Stanford, CA, USA |
| authorships[16].institutions[0].id | https://openalex.org/I97018004 |
| authorships[16].institutions[0].ror | https://ror.org/00f54p054 |
| authorships[16].institutions[0].type | education |
| authorships[16].institutions[0].lineage | https://openalex.org/I97018004 |
| authorships[16].institutions[0].country_code | US |
| authorships[16].institutions[0].display_name | Stanford University |
| authorships[16].author_position | last |
| authorships[16].raw_author_name | Kristen Yeom |
| authorships[16].is_corresponding | False |
| authorships[16].raw_affiliation_strings | Stanford University, Stanford, CA, USA |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://academic.oup.com/neuro-oncology/article-pdf/22/Supplement_3/iii359/34687167/noaa222.357.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | IMG-22. A DEEP LEARNING MODEL FOR AUTOMATIC POSTERIOR FOSSA PEDIATRIC BRAIN TUMOR SEGMENTATION: A MULTI-INSTITUTIONAL STUDY |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T14510 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9539999961853027 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2204 |
| primary_topic.subfield.display_name | Biomedical Engineering |
| primary_topic.display_name | Medical Imaging and Analysis |
| related_works | https://openalex.org/W2365318417, https://openalex.org/W4387015957, https://openalex.org/W2012013780, https://openalex.org/W2957280084, https://openalex.org/W2119664506, https://openalex.org/W3135487440, https://openalex.org/W2099577209, https://openalex.org/W4322616609, https://openalex.org/W4310063436, https://openalex.org/W2327169914 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1093/neuonc/noaa222.357 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S106908163 |
| best_oa_location.source.issn | 1522-8517, 1523-5866 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1522-8517 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Neuro-Oncology |
| best_oa_location.source.host_organization | https://openalex.org/P4310311648 |
| best_oa_location.source.host_organization_name | Oxford University Press |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310311648 |
| best_oa_location.license | cc-by-nc |
| best_oa_location.pdf_url | https://academic.oup.com/neuro-oncology/article-pdf/22/Supplement_3/iii359/34687167/noaa222.357.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Neuro-Oncology |
| best_oa_location.landing_page_url | https://doi.org/10.1093/neuonc/noaa222.357 |
| primary_location.id | doi:10.1093/neuonc/noaa222.357 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S106908163 |
| primary_location.source.issn | 1522-8517, 1523-5866 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1522-8517 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Neuro-Oncology |
| primary_location.source.host_organization | https://openalex.org/P4310311648 |
| primary_location.source.host_organization_name | Oxford University Press |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311648 |
| primary_location.license | cc-by-nc |
| primary_location.pdf_url | https://academic.oup.com/neuro-oncology/article-pdf/22/Supplement_3/iii359/34687167/noaa222.357.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Neuro-Oncology |
| primary_location.landing_page_url | https://doi.org/10.1093/neuonc/noaa222.357 |
| publication_date | 2020-12-01 |
| publication_year | 2020 |
| referenced_works_count | 0 |
| abstract_inverted_index.4 | 206 |
| abstract_inverted_index.a | 65, 151, 161, 216 |
| abstract_inverted_index.2D | 142 |
| abstract_inverted_index.AI | 233 |
| abstract_inverted_index.In | 210 |
| abstract_inverted_index.PF | 71, 107, 223 |
| abstract_inverted_index.We | 140, 155 |
| abstract_inverted_index.an | 127, 144 |
| abstract_inverted_index.as | 131 |
| abstract_inverted_index.by | 126 |
| abstract_inverted_index.in | 10, 16, 45 |
| abstract_inverted_index.is | 53, 235 |
| abstract_inverted_index.of | 13, 37, 105, 123, 204 |
| abstract_inverted_index.on | 160 |
| abstract_inverted_index.or | 34, 117 |
| abstract_inverted_index.to | 27, 63, 185 |
| abstract_inverted_index.we | 214 |
| abstract_inverted_index.720 | 85 |
| abstract_inverted_index.MRI | 87 |
| abstract_inverted_index.Our | 60, 229 |
| abstract_inverted_index.The | 101 |
| abstract_inverted_index.and | 80, 98, 115, 138, 170, 177, 225, 242, 248 |
| abstract_inverted_index.are | 4, 24 |
| abstract_inverted_index.can | 75 |
| abstract_inverted_index.for | 35, 69, 135, 195, 201, 244 |
| abstract_inverted_index.now | 54 |
| abstract_inverted_index.set | 164 |
| abstract_inverted_index.the | 5, 17, 124, 186, 205 |
| abstract_inverted_index.was | 62, 199 |
| abstract_inverted_index.0.83 | 200 |
| abstract_inverted_index.Dice | 166, 193 |
| abstract_inverted_index.With | 42 |
| abstract_inverted_index.deep | 66, 217 |
| abstract_inverted_index.five | 90 |
| abstract_inverted_index.four | 106 |
| abstract_inverted_index.from | 89 |
| abstract_inverted_index.goal | 61 |
| abstract_inverted_index.into | 30, 77, 95 |
| abstract_inverted_index.many | 12 |
| abstract_inverted_index.most | 6 |
| abstract_inverted_index.test | 163 |
| abstract_inverted_index.that | 74, 220, 234 |
| abstract_inverted_index.this | 211 |
| abstract_inverted_index.used | 141 |
| abstract_inverted_index.were | 93 |
| abstract_inverted_index.with | 150 |
| abstract_inverted_index.(AI), | 48 |
| abstract_inverted_index.(PF). | 20 |
| abstract_inverted_index.Brain | 2 |
| abstract_inverted_index.Unet, | 143 |
| abstract_inverted_index.after | 40 |
| abstract_inverted_index.fossa | 19 |
| abstract_inverted_index.human | 190 |
| abstract_inverted_index.model | 68, 136, 196 |
| abstract_inverted_index.scans | 88 |
| abstract_inverted_index.score | 194 |
| abstract_inverted_index.solid | 8 |
| abstract_inverted_index.study | 102 |
| abstract_inverted_index.truth | 188 |
| abstract_inverted_index.tumor | 22, 51, 72, 108, 207, 245 |
| abstract_inverted_index.using | 165, 180 |
| abstract_inverted_index.which | 14 |
| abstract_inverted_index.Manual | 21, 121 |
| abstract_inverted_index.cohort | 103 |
| abstract_inverted_index.common | 7 |
| abstract_inverted_index.create | 64 |
| abstract_inverted_index.expert | 189 |
| abstract_inverted_index.ground | 187 |
| abstract_inverted_index.labels | 134 |
| abstract_inverted_index.linear | 181 |
| abstract_inverted_index.manual | 58, 176 |
| abstract_inverted_index.neural | 147 |
| abstract_inverted_index.recent | 43 |
| abstract_inverted_index.served | 130 |
| abstract_inverted_index.study, | 213 |
| abstract_inverted_index.tumors | 3, 39, 125, 224 |
| abstract_inverted_index.types. | 208 |
| abstract_inverted_index.types: | 109 |
| abstract_inverted_index.volume | 82, 173 |
| abstract_inverted_index.(0–1) | 169 |
| abstract_inverted_index.METHODS | 84 |
| abstract_inverted_index.RESULTS | 183 |
| abstract_inverted_index.applied | 232 |
| abstract_inverted_index.between | 175 |
| abstract_inverted_index.centers | 92 |
| abstract_inverted_index.develop | 15 |
| abstract_inverted_index.diffuse | 111 |
| abstract_inverted_index.divided | 94 |
| abstract_inverted_index.glioma, | 113 |
| abstract_inverted_index.midline | 112 |
| abstract_inverted_index.network | 148 |
| abstract_inverted_index.output. | 83 |
| abstract_inverted_index.outputs | 226 |
| abstract_inverted_index.overall | 192 |
| abstract_inverted_index.present | 215 |
| abstract_inverted_index.provide | 81 |
| abstract_inverted_index.results | 230 |
| abstract_inverted_index.systems | 33, 79 |
| abstract_inverted_index.testing | 99 |
| abstract_inverted_index.without | 56 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Compared | 184 |
| abstract_inverted_index.ResNet50 | 153 |
| abstract_inverted_index.accuracy | 159, 198 |
| abstract_inverted_index.advances | 44 |
| abstract_inverted_index.assessed | 156 |
| abstract_inverted_index.compared | 171 |
| abstract_inverted_index.encoder. | 154 |
| abstract_inverted_index.feasible | 55 |
| abstract_inverted_index.held-out | 162 |
| abstract_inverted_index.learning | 67, 218 |
| abstract_inverted_index.optimize | 28 |
| abstract_inverted_index.register | 76 |
| abstract_inverted_index.required | 26 |
| abstract_inverted_index.surgical | 31, 249 |
| abstract_inverted_index.therapy. | 41 |
| abstract_inverted_index.training | 137 |
| abstract_inverted_index.truth” | 133 |
| abstract_inverted_index.MRI-based | 50 |
| abstract_inverted_index.algorithm | 219 |
| abstract_inverted_index.attending | 128 |
| abstract_inverted_index.automated | 49, 70 |
| abstract_inverted_index.automatic | 202 |
| abstract_inverted_index.brainstem | 116 |
| abstract_inverted_index.comprised | 104 |
| abstract_inverted_index.datasets. | 100 |
| abstract_inverted_index.pediatric | 91 |
| abstract_inverted_index.pilocytic | 119 |
| abstract_inverted_index.planning. | 250 |
| abstract_inverted_index.posterior | 18 |
| abstract_inverted_index.requiring | 57 |
| abstract_inverted_index.training, | 96 |
| abstract_inverted_index.ventricle | 157 |
| abstract_inverted_index.“ground | 132 |
| abstract_inverted_index.BACKGROUND | 1 |
| abstract_inverted_index.artificial | 46 |
| abstract_inverted_index.augmenting | 239 |
| abstract_inverted_index.cerebellar | 118 |
| abstract_inverted_index.childhood, | 11 |
| abstract_inverted_index.clinically | 236 |
| abstract_inverted_index.delineates | 222 |
| abstract_inverted_index.frequently | 25 |
| abstract_inverted_index.navigation | 32, 78 |
| abstract_inverted_index.similarity | 167 |
| abstract_inverted_index.volumetric | 227 |
| abstract_inverted_index.CONCLUSIONS | 209 |
| abstract_inverted_index.applicable, | 237 |
| abstract_inverted_index.calculation | 174 |
| abstract_inverted_index.coefficient | 168 |
| abstract_inverted_index.delineation | 203 |
| abstract_inverted_index.demonstrate | 231 |
| abstract_inverted_index.ependymoma, | 114 |
| abstract_inverted_index.evaluation, | 246 |
| abstract_inverted_index.evaluation. | 139 |
| abstract_inverted_index.performance | 197 |
| abstract_inverted_index.potentially | 238 |
| abstract_inverted_index.pre-trained | 152 |
| abstract_inverted_index.regression. | 182 |
| abstract_inverted_index.validation, | 97 |
| abstract_inverted_index.ventricular | 172 |
| abstract_inverted_index.astrocytoma. | 120 |
| abstract_inverted_index.information. | 228 |
| abstract_inverted_index.intelligence | 47 |
| abstract_inverted_index.malignancies | 9 |
| abstract_inverted_index.measurements | 23 |
| abstract_inverted_index.pre-surgical | 86 |
| abstract_inverted_index.registration | 29 |
| abstract_inverted_index.segmentation | 52, 73, 122, 158 |
| abstract_inverted_index.surveillance | 36 |
| abstract_inverted_index.architecture, | 149 |
| abstract_inverted_index.automatically | 221 |
| abstract_inverted_index.convolutional | 146 |
| abstract_inverted_index.measurements. | 59 |
| abstract_inverted_index.model-derived | 178 |
| abstract_inverted_index.neurosurgeons | 243 |
| abstract_inverted_index.nonresectable | 38 |
| abstract_inverted_index.radiologists, | 240 |
| abstract_inverted_index.segmentation, | 191 |
| abstract_inverted_index.segmentations | 179 |
| abstract_inverted_index.surveillance, | 247 |
| abstract_inverted_index.encoder-decoder | 145 |
| abstract_inverted_index.medulloblastoma, | 110 |
| abstract_inverted_index.neuroradiologist | 129 |
| abstract_inverted_index.neuro-oncologists, | 241 |
| abstract_inverted_index.multi-institutional | 212 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 17 |
| citation_normalized_percentile.value | 0.13945615 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |