activity
20182020
most citedContrast Phase Classification with a Generative Adversarial Network

3 citations · 4 across the 3 of their papers we have counts for

collaborators

6 papers

eess.IV2020

Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives

Yuchen Xu, Olivia Tang, Yucheng Tang +9

Segmentation of abdominal computed tomography(CT) provides spatial context, morphological properties, and a framework for tissue-specific radiomics to guide quantitative Radiologic…

cs.CV20201 cited

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…

eess.IV20193 cited

Contrast Phase Classification with a Generative Adversarial Network

Yucheng Tang, Ho Hin Lee, Yuchen Xu +10

Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context…

eess.IV2019

Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision

Ho Hin Lee, Yucheng Tang, Olivia Tang +9

Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that the systems are performing as intended, as well as identifying and e…

cs.CV2018

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…

cs.CV2018

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…