TY - GEN
T1 - Cross-Modality Brain Structures Image Segmentation for the Radiotherapy Target Definition and Plan Optimization
AU - Shusharina, Nadya
AU - Bortfeld, Thomas
AU - Cardenas, Carlos
AU - De, Brian
AU - Diao, Kevin
AU - Hernandez, Soleil
AU - Liu, Yufei
AU - Maroongroge, Sean
AU - Söderberg, Jonas
AU - Soliman, Moaaz
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - This paper summarizes results of the International Challenge “Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images”, ABCs, organized in conjunction with the MICCAI 2020 conference. Eighteen segmentation algorithms were trained on a set of 45 CT, T1 -weighted MR, and T2 -weighted FLAIR MR post-operative images of glioblastoma and low-grade glioma patients. Manual delineations were provided for the brain structures: falx cerebri, tentorium cerebelli, transverse and sagittal brain sinuses, ventricles, cerebellum (Task 1) and for the brainstem, structures of visual pathway, optic chiasm, optic nerves, and eyes, structures of auditory pathway, cochlea, and lacrimal glands (Task 2). The algorithms were tested on a set of 15 cases and received the final score for predicting segmentation on a separate 15 case image set. Multi-rater delineations with seven raters were obtained for the three cases. The results suggest that neural network based algorithms have become a successful technique of brain structure segmentation, and closely approach human performance in segmenting specific brain structures.
AB - This paper summarizes results of the International Challenge “Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images”, ABCs, organized in conjunction with the MICCAI 2020 conference. Eighteen segmentation algorithms were trained on a set of 45 CT, T1 -weighted MR, and T2 -weighted FLAIR MR post-operative images of glioblastoma and low-grade glioma patients. Manual delineations were provided for the brain structures: falx cerebri, tentorium cerebelli, transverse and sagittal brain sinuses, ventricles, cerebellum (Task 1) and for the brainstem, structures of visual pathway, optic chiasm, optic nerves, and eyes, structures of auditory pathway, cochlea, and lacrimal glands (Task 2). The algorithms were tested on a set of 15 cases and received the final score for predicting segmentation on a separate 15 case image set. Multi-rater delineations with seven raters were obtained for the three cases. The results suggest that neural network based algorithms have become a successful technique of brain structure segmentation, and closely approach human performance in segmenting specific brain structures.
KW - Cross-modality
KW - Deep learning
KW - Radiotherapy target
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85104423130&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85104423130&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-71827-5_1
DO - 10.1007/978-3-030-71827-5_1
M3 - Conference contribution
AN - SCOPUS:85104423130
SN - 9783030718268
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 15
BT - Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data - MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Shusharina, Nadya
A2 - Heinrich, Mattias P.
A2 - Huang, Ruobing
PB - Springer Science and Business Media Deutschland GmbH
T2 - Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge, TN-SCUI 2020 held in conjunction with 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Y2 - 4 October 2020 through 8 October 2020
ER -