260 citations · 650 across the 16 of their papers we have counts for
16 papers
Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge
Jun Ma, Yao Zhang, Song Gu +26
Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to automa…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
Jun Ma, Ronald Xie, Shamini Ayyadhury +37
Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or req…
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Lei Li, Fuping Wu, Sihan Wang +29
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardiu…
Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
Alain Lalande, Zhihao Chen, Thibaut Pommier +30
A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization the…
Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?
Jun Ma
Segmentation is one of the most important and popular tasks in medical image analysis, which plays a critical role in disease diagnosis, surgical planning, and prognosis evaluation…