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8 results on '"Bella Specktor-Fadida"'

5. Fetal Brain Tissue Annotation and Segmentation Challenge Results.

Author

Kelly Payette, Hongwei Li 0004, Priscille de Dumast, Roxane Licandro, Hui Ji, Md Mahfuzur Rahman Siddiquee, Daguang Xu, Andriy Myronenko, Hao Liu, Yuchen Pei, Lisheng Wang, Ying Peng, Juanying Xie, Huiquan Zhang, Guiming Dong, Hao Fu, Guotai Wang, ZunHyan Rieu, Donghyeon Kim, Hyun Gi Kim, Davood Karimi, Ali Gholipour, Helena R. Torres, Bruno Oliveira 0002, João L. Vilaça, Yang Lin, Netanell Avisdris, Ori Ben-Zvi, Dafna Ben-Bashat, Lucas Fidon, Michael Aertsen, Tom Vercauteren, Daniel Sobotka, Georg Langs, Mireia Alenyà, Maria Inmaculada Villanueva, Oscar Camara 0001, Bella Specktor-Fadida, Leo Joskowicz, Weibin Liao, Lv Yi, Xuesong Li 0003, Moona Mazher, Abdul Qayyum, Domenec Puig, Hamza Kebiri, Zelin Zhang, Xinyi Xu, Dan Wu, KuanLun Liao, YiXuan Wu, JinTai Chen, Yunzhi Xu, Li Zhao, Lana Vasung, Bjoern H. Menze, Meritxell Bach Cuadra, and András Jakab

Published
2022
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6. Fetal brain tissue annotation and segmentation challenge results.

Author

Kelly Payette, Hongwei Bran Li, Priscille de Dumast, Roxane Licandro, Hui Ji, Md Mahfuzur Rahman Siddiquee, Daguang Xu, Andriy Myronenko, Hao Liu, Yuchen Pei, Lisheng Wang, Ying Peng, Juanying Xie, Huiquan Zhang, Guiming Dong, Hao Fu, Guotai Wang, ZunHyan Rieu, Donghyeon Kim, Hyun Gi Kim, Davood Karimi, Ali Gholipour, Helena R. Torres, Bruno Oliveira 0002, João L. Vilaça, Yang Lin, Netanell Avisdris, Ori Ben-Zvi, Dafna Ben-Bashat, Lucas Fidon, Michael Aertsen, Tom Vercauteren, Daniel Sobotka, Georg Langs, Mireia Alenyà, Maria Inmaculada Villanueva, Oscar Camara 0001, Bella Specktor-Fadida, Leo Joskowicz, Liao Weibin, Lv Yi, Xuesong Li 0003, Moona Mazher, Abdul Qayyum, Domenec Puig, Hamza Kebiri, Zelin Zhang, Xinyi Xu, Dan Wu, KuanLun Liao, YiXuan Wu, JinTai Chen, Yunzhi Xu, Li Zhao, Lana Vasung, Bjoern H. Menze, Meritxell Bach Cuadra, and András Jakab

Published
2023
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8. A Bootstrap Self-training Method for Sequence Transfer: State-of-the-Art Placenta Segmentation in fetal MRI

Author

Shai Ferster-Kveller, Leo Joskowicz, Liat Ben-Sira, Dafna Ben-Bashat, Daphna Link-Sourani, Elka Miller, and Bella Specktor-Fadida

Subjects
Test case, Observer (quantum physics), Bootstrapping (electronics), Computer science, business.industry, Deep learning, Dice, Pattern recognition, Segmentation, Artificial intelligence, business, Transfer of learning, Sequence (medicine)
Abstract

Quantitative volumetric evaluation of the placenta in fetal MRI scans is an important component of the fetal health evaluation. However, manual segmentation of the placenta is a time-consuming task that requires expertise and suffers from high observer variability. Deep learning methods for automatic segmentation are effective but require manually annotated datasets for each scanning sequence. We present a new method for bootstrapping automatic placenta segmentation by deep learning on different MRI sequences. The method consists of automatic placenta segmentation with two networks trained on labeled cases of one sequence followed by automatic adaptation using self-training of the same network to a new sequence with new unlabeled cases of this sequence. It uses a novel combined contour and soft Dice loss function for both the placenta ROI detection and segmentation networks. Our experimental studies for the FIESTA sequence yields a Dice score of 0.847 on 21 test cases with only 16 cases in the training set. Transfer to the TRUFI sequence yields a Dice score of 0.78 on 15 test cases, a significant improvement over the network results without transfer learning. The contour Dice loss and self-training approach achieve state-of-the art placenta segmentation results by sequence transfer bootstrapping.

Published
2021
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