Search Results for author: Tsung-Ying Ho

Found 10 papers, 0 papers with code

Interactive Radiotherapy Target Delineation with 3D-Fused Context Propagation

no code implementations12 Dec 2020 Chun-Hung Chao, Hsien-Tzu Cheng, Tsung-Ying Ho, Le Lu, Min Sun

The proposed method is evaluated on two published radiotherapy target contouring datasets of nasopharyngeal and esophageal cancer.

Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network

no code implementations29 Aug 2020 Chun-Hung Chao, Zhuotun Zhu, Dazhou Guo, Ke Yan, Tsung-Ying Ho, Jinzheng Cai, Adam P. Harrison, Xianghua Ye, Jing Xiao, Alan Yuille, Min Sun, Le Lu, Dakai Jin

Specifically, we first utilize a 3D convolutional neural network with ROI-pooling to extract the GTV$_{LN}$'s instance-wise appearance features.

Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy

no code implementations27 Aug 2020 Zhuotun Zhu, Dakai Jin, Ke Yan, Tsung-Ying Ho, Xianghua Ye, Dazhou Guo, Chun-Hung Chao, Jing Xiao, Alan Yuille, Le Lu

Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance.

Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification

no code implementations27 May 2020 Zhuotun Zhu, Ke Yan, Dakai Jin, Jinzheng Cai, Tsung-Ying Ho, Adam P. Harrison, Dazhou Guo, Chun-Hung Chao, Xianghua Ye, Jing Xiao, Alan Yuille, Le Lu

We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymph nodes (OSLNs), which has not been studied before as a computational task.

Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search

no code implementations CVPR 2020 Dazhou Guo, Dakai Jin, Zhuotun Zhu, Tsung-Ying Ho, Adam P. Harrison, Chun-Hung Chao, Jing Xiao, Alan Yuille, Chien-Yu Lin, Le Lu

This is the goal of our work, where we introduce stratified organ at risk segmentation (SOARS), an approach that stratifies OARs into anchor, mid-level, and small & hard (S&H) categories.

Neural Architecture Search

Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk

no code implementations4 Sep 2019 Dakai Jin, Dazhou Guo, Tsung-Ying Ho, Adam P. Harrison, Jing Xiao, Chen-Kan Tseng, Le Lu

Clinical target volume (CTV) delineation from radiotherapy computed tomography (RTCT) images is used to define the treatment areas containing the gross tumor volume (GTV) and/or sub-clinical malignant disease for radiotherapy (RT).

Data Augmentation

Radiotherapy Target Contouring with Convolutional Gated Graph Neural Network

no code implementations5 Apr 2019 Chun-Hung Chao, Yen-Chi Cheng, Hsien-Tzu Cheng, Chi-Wen Huang, Tsung-Ying Ho, Chen-Kan Tseng, Le Lu, Min Sun

Instead, inspired by the treating methodology of considering meaningful information across slices, we used Gated Graph Neural Network to frame this problem more efficiently.

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