AttentionAnatomy: A unified framework for whole-body organs at risk segmentation using multiple partially annotated datasets

“Comparing images using the hausdorff distance,”

Original

Daniel P Huttenlocher, Gregory A Klanderman, and William J Rucklidge, · 1993

Earlier work this paper cites.

“U-net: Convolutional networks for biomedical image segmentation,”

Olaf Ronneberger, Philipp Fischer, and Thomas Brox, · 2015

Earlier work this paper cites.

“V-net: Fully convolutional neural networks for volumetric medical image segmentation,”

Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi, · 2016

Earlier work this paper cites.

“Tversky loss function for image segmentation using 3d fully convolutional deep networks,”

Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, and Ali Gholipour, · 2017

Cited alongside, same era.

“Focal loss for dense object detection,”

Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár, · 2017

Cited alongside, same era.

“Segmentation of head and neck organs at risk using cnn with batch dice loss,”

Oldřich Kodym, Michal Španěl, and Adam Herout, · 2018

Cited alongside, same era.

“Clinically applicable deep learning framework for organs at risk delineation in ct images,”

Hao Tang, Xuming Chen, Yang Liu, Zhipeng Lu, Junhua You, Mingzhou Yang, Shengyu Yao, Guoqi Zhao, Yi Xu, Tingfeng Chen, et al., · 2019

Later among the works it cites.

“Anatomynet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy,”

Wentao Zhu, Yufang Huang, Liang Zeng, Xuming Chen, Yong Liu, Zhen Qian, Nan Du, Wei Fan, and Xiaohui Xie, · 2019

Later among the works it cites.