Overview of the VISCERAL Challenge at ISBI 2015
نویسندگان
چکیده
This is an overview paper describing the data and evaluation scheme of the VISCERAL Segmentation Challenge at ISBI 2015. The challenge was organized on a cloud-based virtualmachine environment, where each participant could develop and submit their algorithms. The dataset contains up to 20 anatomical structures annotated in a training and a test set consisting of CT and MR images with and without contrast enhancement. The test-set is not accessible to participants, and the organizers run the virtual-machines with submitted segmentation methods on the test data. The results of the evaluation are then presented to the participant, who can opt to make it public on the challenge leaderboard displaying 20 segmentation quality metrics per-organ and permodality. Dice coefficient and mean-surface distance are presented herein as representative quality metrics. As a continuous evaluation platform, our segmentation challenge leaderboard will be open beyond the duration of the VISCERAL project.
منابع مشابه
VISCERAL — VISual Concept Extraction challenge in RAdioLogy: ISBI 2014 Challenge Organization
The VISual Concept Extraction challenge in RAdioLogy (VISCERAL) project has been developed as a cloud–based infrastructure for the evaluation of medical image data in large data sets. As part of this project, the ISBI 2014 (International Symposium for Biomedical Imaging) challenge was organized using the VISCERAL data set and shared cloud– framework. Two tasks were selected to exploit and compa...
متن کاملISBI 2014 VISCERAL Organ Segmentation and Landmark Detection
The VISual Concept Extraction challenge in RAdioLogy (VISCERAL) project has been developed as a cloud–based infrastructure for the evaluation of medical image data in large data sets. As part of this project, the ISBI 2014 (International Symposium for Biomedical Imaging) challenge was organized using the VISCERAL data set and shared cloud– framework. Two tasks were selected to exploit and compa...
متن کاملFully Automatic Multi-Organ Segmentation Based on Multi-Boost Learning and Statistical Shape Model Search
In this paper, an automatic multi-organ segmentation based on multi-boost learning and statistical shape model search was proposed. First, simple but robust Multi-Boost Classifier was trained to hierarchically locate and pre-segment multiple organs. To ensure the generalization ability of the classifier relative location information between organs, organ and whole body is exploited. Left lung a...
متن کاملGood Features for Reliable Registration in Multi-Atlas Segmentation
This work presents a method for multi-organ segmentation in whole-body CT images based on a multi-atlas approach. A robust and efficient feature-based registration technique is developed which uses sparse organ specific features that are learnt based on their ability to register different organ types accurately. The best fitted feature points are used in RANSAC to estimate an affine transformat...
متن کاملEfficient and fully automatic segmentation of the lungs in CT volumes
The segmentation of lung volumes constitutes the first step for most computer–aided systems for lung diseases. CT (Computed Tomography) is the most common imaging technique used by these systems, so fast and accurate methods are needed to for allow early and reliable analysis. In this paper, an efficient and fully automatic method for the segmentation of the lung volumes in CT is presented. Thi...
متن کامل