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CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT
Bo Zhou, Adam P. Harrison, Jiawen Yao +4
As the demand for more descriptive machine learning models grows within medical imaging, bottlenecks due to data paucity will exacerbate. Thus, collecting enough large-scale data w…
Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk
Dakai Jin, Dazhou Guo, Tsung-Ying Ho +4
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 su…
Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT using Two-Stream Chained 3D Deep Network Fusion
Dakai Jin, Dazhou Guo, Tsung-Ying Ho +4
Gross tumor volume (GTV) segmentation is a critical step in esophageal cancer radiotherapy treatment planning. Inconsistencies across oncologists and prohibitive labor costs motiva…
Weakly Supervised Universal Fracture Detection in Pelvic X-rays
Yirui Wang, Le Lu, Chi-Tung Cheng +5
Hip and pelvic fractures are serious injuries with life-threatening complications. However, diagnostic errors of fractures in pelvic X-rays (PXRs) are very common, driving the dema…
Radiotherapy Target Contouring with Convolutional Gated Graph Neural Network
Chun-Hung Chao, Yen-Chi Cheng, Hsien-Tzu Cheng +5
Tomography medical imaging is essential in the clinical workflow of modern cancer radiotherapy. Radiation oncologists identify cancerous tissues, applying delineation on treatment…
Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data
Ling Zhang, Le Lu, Xiaosong Wang +4
Prognostic tumor growth modeling via volumetric medical imaging observations can potentially lead to better outcomes of tumor treatment and surgical planning. Recent advances of co…