Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study

no code implementations • 1 Nov 2021 • Dazhou Guo, Jia Ge, Xianghua Ye, Senxiang Yan, Yi Xin, Yuchen Song, Bing-shen Huang, Tsung-Min Hung, Zhuotun Zhu, Ling Peng, Yanping Ren, Rui Liu, Gong Zhang, Mengyuan Mao, Xiaohua Chen, Zhongjie Lu, Wenxiang Li, Yuzhen Chen, Lingyun Huang, Jing Xiao, Adam P. Harrison, Le Lu, Chien-Yu Lin, Dakai Jin, Tsung-Ying Ho

Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications.

Neural Architecture Search Segmentation

Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT

no code implementations • 11 Oct 2021 • Xianghua Ye, Dazhou Guo, Chen-Kan Tseng, Jia Ge, Tsung-Min Hung, Ping-Ching Pai, Yanping Ren, Lu Zheng, Xinli Zhu, Ling Peng, Ying Chen, Xiaohua Chen, Chen-Yu Chou, Danni Chen, Jiaze Yu, Yuzhen Chen, Feiran Jiao, Yi Xin, Lingyun Huang, Guotong Xie, Jing Xiao, Le Lu, Senxiang Yan, Dakai Jin, Tsung-Ying Ho

252 institution-1 patients had a treatment planning-CT (pCT) and a pair of diagnostic FDG-PETCT; 354 patients from other 3 institutions had only pCT.

DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search

no code implementations • 20 Sep 2021 • Dazhou Guo, Xianghua Ye, Jia Ge, Xing Di, Le Lu, Lingyun Huang, Guotong Xie, Jing Xiao, Zhongjie Liu, Ling Peng, Senxiang Yan, Dakai Jin

Lymph node station (LNS) delineation from computed tomography (CT) scans is an indispensable step in radiation oncology workflow.

Computed Tomography (CT) Organ Segmentation

Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network

no code implementations • 20 Apr 2020 • Congcong Wen, Xiang Li, Xiaojing Yao, Ling Peng, Tianhe Chi

To achieve point cloud classification, previous studies proposed point cloud deep learning models that can directly process raw point clouds based on PointNet-like architectures.

Deep Learning General Classification +3

Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud Classification

1 code implementation • 19 Aug 2019 • Congcong Wen, Lina Yang, Ling Peng, Xiang Li, Tianhe Chi

In this paper, we proposed a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds for semantic labeling.

General Classification Line Detection +1