Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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C. Kroll, F. Milletari, N. Navab, A. Ahmadi
Coupling Convolutional Neural Networks and Hough Voting for Robust Segmentation of Ultrasound Volumes
Accepted at GCPR 2016 (bib)

This paper analyses the applicability and performance of Convolutional Neural Networks (CNN) to localise and segment anatomical structures in medical volumes under clinically realistic constraints: small amount of available training data, the need of a short processing time and limited computational resources. Our segmentation approach employs CNNs for simultaneous classification and feature extraction. A Hough voting strategy has been developed in order to automatically localise and segment the anatomy of interest. Our results show (i) improved robustness, due to the inclusion of prior shape knowledge, (ii) highly accurate segmentation even when only small datasets are available during training, (iii) speed and computational requirements that match those that are usually present in clinical settings.
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