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Found 8 papers, 6 papers with code
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
We propose U-Time, a fully feed-forward deep learning approach to physiological time series segmentation developed for the analysis of sleep data.
One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation
The system relies on multi-planar data augmentation which facilitates the application of a single 2D architecture based on the familiar U-Net.
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
Multi-planar 3D knee MRI segmentation via UNet inspired architectures
By evaluating the architectures in a different dimensionality, embedded in a different method, and for a different task, we aimed to evaluate if any of these UNet alternatives are promising as a new golden standard that generalizes even better than the UNet.
Local Gamma Augmentation for Ischemic Stroke Lesion Segmentation on MRI
Data augmentation techniques can compensate for a lack of training samples.
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet
Automated segmentation of distinct tumor regions is critical for accurate diagnosis and treatment planning in pediatric brain tumors.
