A Hybrid Learning Approach For Semantic Labeling Of Cardiac Ct Slices And Recognition Of Body Position
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)(2016)
摘要
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 hand-crafted 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
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关键词
cardiac CT,slice level recognition,image category classification
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