Abstract #4736

Technical Considerations for Semantic Segmentation in Magnetic Resonance Imaging using Deep Convolutional Neural Networks: A Case Study in Femoral Cartilage Segmentation

Arjun D. Desai¹, Garry E. Gold^1,2,3, Brian A. Hargreaves^1,2,4, and Akshay S. Chaudhari¹

¹Radiology, Stanford University, Stanford, CA, United States, ²Bioengineering, Stanford University, Stanford, CA, United States, ³Orthopedic Surgery, Stanford University, Stanford, CA, United States, ⁴Electrical Engineering, Stanford University, Stanford, CA, United States

Deep convolutional neural networks (CNNs) have shown promise in challenging tissue segmentation problems in medical imaging. However, due to the large size of these networks and stochasticity of the training process, the factors affecting CNN performance are difficult to analytically model. In this study, we numerically evaluate the impact of network architecture and characteristics of training data on network performance for segmenting femoral cartilage. We show that extensive training of several common network architectures yields comparable performance and that somewhat optimal network generalizability can be achieved with limited training data.

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