3 citations · 4 across the 2 of their papers we have counts for
3 papers
eess.IV2020★ 3 cited
Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation
Hao Tang, Xingwei Liu, Kun Han +6
Multi-organ segmentation is one of most successful applications of deep learning in medical image analysis. Deep convolutional neural nets (CNNs) have shown great promise in achiev…
eess.IV2020★ 1 cited
AttentionAnatomy: A unified framework for whole-body organs at risk segmentation using multiple partially annotated datasets
Shanlin Sun, Yang Liu, Narisu Bai +5
Organs-at-risk (OAR) delineation in computed tomography (CT) is an important step in Radiation Therapy (RT) planning. Recently, deep learning based methods for OAR delineation have…
cs.CV2018
AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy
Wentao Zhu, Yufang Huang, Liang Zeng +6
Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generati…