most citedLearning-Based Cost Functions for 3D and 4D Multi-Surface Multi-Object Segmentation of Knee MRI: Data from the Osteoarthritis Initiative

49 citations · 77 across the 3 of their papers we have counts for

collaborators

4 papers

eess.IV2020

Fully Automated 3D Segmentation of MR-Imaged Calf Muscle Compartments: Neighborhood Relationship Enhanced Fully Convolutional Network

Zhihui Guo, Honghai Zhang, Zhi Chen +5

Automated segmentation of individual calf muscle compartments from 3D magnetic resonance (MR) images is essential for developing quantitative biomarkers for muscular disease progre…

cs.CV20194 cited

Just-Enough Interaction Approach to Knee MRI Segmentation: Data from the Osteoarthritis Initiative

Satyananda Kashyap, Honghai Zhang, Milan Sonka

State-of-the-art automated segmentation algorithms are not 100\% accurate especially when segmenting difficult to interpret datasets like those with severe osteoarthritis (OA). We…

cs.CV201924 cited

Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative

Satyananda Kashyap, Ipek Oguz, Honghai Zhang +1

We present a fully automated learning-based approach for segmenting knee cartilage in the presence of osteoarthritis (OA). The algorithm employs a hierarchical set of two random fo…

cs.CV201949 cited

Learning-Based Cost Functions for 3D and 4D Multi-Surface Multi-Object Segmentation of Knee MRI: Data from the Osteoarthritis Initiative

Satyananda Kashyap, Honghai Zhang, Karan Rao +1

A fully automated knee MRI segmentation method to study osteoarthritis (OA) was developed using a novel hierarchical set of random forests (RF) classifiers to learn the appearance…