Publication
ISBI 2016
Conference paper

A hybrid learning approach for semantic labeling of cardiac CT slices and recognition of body position

View publication

Abstract

We work towards efficient methods of categorizing visual content in medical images as a precursor step to segmentation and anatomy recognition. In this paper, we address the problem of automatic detection of level/position for a given cardiac CT slice. Specifically, we divide the body area depicted in chest CT into nine semantic categories each representing an area most relevant to the study of a disease and/or key anatomic cardiovascular feature. Using a set of handcrafted image features together with features derived form a deep convolutional neural network (CNN), we build a classification scheme to map a given CT slice to the relevant level. Each feature group is used to train a separate support vector machine classifier. The resulting labels are then combined in a linear model, also learned from training data. We report margin zero and margin one accuracy of 91.7% and 98.8% and show that this hybrid approach is a very effective methodology for assigning a given CT image to a relatively narrow anatomic window.

Date

15 Jun 2016

Publication

ISBI 2016

Authors

Share