24 citations · 51 across the 8 of their papers we have counts for
5 papers · 1 filter
Outlier Guided Optimization of Abdominal Segmentation
Yuchen Xu, Olivia Tang, Yucheng Tang +9
Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive research interest. It presents a substantial challenge in medical image proc…
Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks
Yuankai Huo, Zhoubing Xu, Shunxing Bao +8
The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen disease. Automated segmentation methods are essential to…
SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth
Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon +6
A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically requir…
Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth
Yuankai Huo, Zhoubing Xu, Shunxing Bao +3
A lack of generalizability is one key limitation of deep learning based segmentation. Typically, one manually labels new training images when segmenting organs in different imaging…
Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks
Yuankai Huo, Zhoubing Xu, Shunxing Bao +7
Spleen volume estimation using automated image segmentation technique may be used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance Imaging (MRI) scans. In…