activity
20192022
most citedOrgan at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search

14 citations · 57 across the 11 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

eess.IV2020

Interactive Radiotherapy Target Delineation with 3D-Fused Context Propagation

Chun-Hung Chao, Hsien-Tzu Cheng, Tsung-Ying Ho +2

Gross tumor volume (GTV) delineation on tomography medical imaging is crucial for radiotherapy planning and cancer diagnosis. Convolutional neural networks (CNNs) has been predomin…

cs.CV2020★ 4 cited

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

Chun-Hung Chao, Zhuotun Zhu, Dazhou Guo +10

Determining the spread of GTV is essential in defining the respective resection or irradiating regions for the downstream workflows of surgical resection and radiotherapy fo…

eess.IV2020★ 3 cited

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

Zhuotun Zhu, Dakai Jin, Ke Yan +7

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

cs.CV2020★ 7 cited

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

Zhuotun Zhu, Ke Yan, Dakai Jin +9

Finding and identifying scatteredly-distributed, small, and critically important objects in 3D oncology images is very challenging. We focus on the detection and segmentation of on…

cs.CV2020★ 14 cited

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

Dazhou Guo, Dakai Jin, Zhuotun Zhu +7

OAR segmentation is a critical step in radiotherapy of head and neck (H&N) cancer, where inconsistencies across radiation oncologists and prohibitive labor costs motivate automated…