1.3k citations · 1.5k across the 6 of their papers we have counts for
6 papers
Diffusion MRI preprocessing affects ADC estimation and automatic PI-RADS v2.1 classification in bi-parametric prostate MRI
Christos Kanakis, Mathias Perslev, Tim Schakel +4
Diffusion-weighted imaging (DWI) is acquired as part of bi-parametric prostate MRI, but suffers from artifacts that degrade downstream quantitative and diagnostic performance. Whil…
Local Gamma Augmentation for Ischemic Stroke Lesion Segmentation on MRI
Jon Middleton, Marko Bauer, Kaining Sheng +5
The identification and localisation of pathological tissues in medical images continues to command much attention among deep learning practitioners. When trained on abundant datase…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Arjun D. Desai, Francesco Caliva, Claudia Iriondo +26
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthriti…
One Network to Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation
Mathias Perslev, Erik Bjørnager Dam, Akshay Pai +1
Many recent medical segmentation systems rely on powerful deep learning models to solve highly specific tasks. To maximize performance, it is standard practice to evaluate numerous…
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
Mathias Perslev, Michael Hejselbak Jensen, Sune Darkner +2
Neural networks are becoming more and more popular for the analysis of physiological time-series. The most successful deep learning systems in this domain combine convolutional and…