Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.
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.
Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems.
Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest, like semantic segmentation.
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization.
We demonstrate the model's clinical applicability by assessing its performance on a test set of 21 CT scans from clinical practice, each with the 21 OARs segmented by two independent experts.
To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.
This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images.
Ranked #34 on 3D Part Segmentation on ShapeNet-Part (Instance Average IoU metric)
Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer.
There is large consent that successful training of deep networks requires many thousand annotated training samples.
Ranked #1 on Medical Image Segmentation on ISBI 2012 EM Segmentation (Warping Error metric)
With volumetric data from widefield fluorescence microscopy, many emerging questions in biological and biomedical research are being investigated.