Overview of VISCERAL Benchmarks in Intelligent Medical Data Analysis

Abstract

Background: Large datasets of annotated medical images are important to train machine learning algorithms. Segmentation is the first step in many decision support applications. Objectives: Learning objectives include: Outline: Organ segmentation is the first step in many decision support tools in radiology. To obtain good segmentation results, most often, large annotated datasets need to be available. Also, most segmentation algorithms are very organ-specific and modality-specific. The VISCERAL benchmark makes data from 20 organs available both with and without contrast agent and for CT and MR. An architecture for manual segmentation including quality control was developed for this purpose.