Abstract
Subject-specific segmentation for medical images plays a critical role in translating medical image computing techniques to routine clinical practice. Many current segmentation methods, however, are still focused on building general models, and thus lack the generalizability for unseen, particularly pathological data. In this paper, a reinforcement learning algorithm is proposed to integrate specific human expert behavior for segmenting subject-specific data. The algorithm uses a generic two-layer reinforcement learning framework and incorporates shape instantiation to constrain the target shape geometrically. The learning occurs in the background when the user segments the image in real-time, thus eliminating the need to prepare subject-specific training data. Detailed validation of the algorithm on hypertrophic cardiomyopathy (HCM) datasets demonstrates improved segmentation accuracy, reduced user-input, and thus the potential clinical value of the proposed algorithm.
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Wang, L., Lee, SL., Merrifield, R., Yang, GZ. (2011). Subject-Specific Cardiac Segmentation Based on Reinforcement Learning with Shape Instantiation. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_37
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DOI: https://doi.org/10.1007/978-3-642-24319-6_37
Publisher Name: Springer, Berlin, Heidelberg
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