260 citations · 507 across the 14 of their papers we have counts for
9 papers · 1 filter
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…
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation
Zhe Xu, Yixin Wang, Donghuan Lu +6
Semi-supervised learning has substantially advanced medical image segmentation since it alleviates the heavy burden of acquiring the costly expert-examined annotations. Especially,…
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities
Yixin Wang, Yang Zhang, Yang Liu +6
Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is clinically relevant in diagnoses, prognoses and surgery treatment, which requires multiple modalities…
Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation
Zhe Xu, Donghuan Lu, Yixin Wang +5
Manually segmenting the hepatic vessels from Computer Tomography (CT) is far more expertise-demanding and laborious than other structures due to the low-contrast and complex morpho…
Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer
Yao Zhang, Jiawei Yang, Feng Hou +6
Accurate segmentation of cardiac structures can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice. However,…
Modality-Pairing Learning for Brain Tumor Segmentation
Yixin Wang, Yao Zhang, Feng Hou +5
Automatic brain tumor segmentation from multi-modality Magnetic Resonance Images (MRI) using deep learning methods plays an important role in assisting the diagnosis and treatment…
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation
Jun Ma, Yixin Wang, Xingle An +10
Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based…
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge
Nicholas Heller, Fabian Isensee, Klaus H. Maier-Hein +38
There is a large body of literature linking anatomic and geometric characteristics of kidney tumors to perioperative and oncologic outcomes. Semantic segmentation of these tumors a…
Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes
Yao Zhang, Yixin Wang, Feng Hou +4
Automated segmentation of kidney and tumor from 3D CT scans is necessary for the diagnosis, monitoring, and treatment planning of the disease. In this paper, we describe a two-stag…