most citedModified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images

451 citations · 731 across the 3 of their papers we have counts for

collaborators

4 papers

physics.med-ph2020

Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface

Charles Huang, Yong Yang, Neil Panjwani +2

Objective: Radiation therapy treatment planning is a time-consuming, iterative process with potentially high inter-planner variability. Fully automated treatment planning processes…

cs.CV20194 cited

Atlas Based Segmentations via Semi-Supervised Diffeomorphic Registrations

Charles Huang, Masoud Badiei, Hyunseok Seo +5

Purpose: Segmentation of organs-at-risk (OARs) is a bottleneck in current radiation oncology pipelines and is often time consuming and labor intensive. In this paper, we propose an…

eess.IV2019276 cited

Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications

Hyunseok Seo, Masoud Badiei Khuzani, Varun Vasudevan +5

In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this re…

eess.IV2019451 cited

Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images

Hyunseok Seo, Charles Huang, Maxime Bassenne +2

Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it…