Deep Reinforcement Learning for Organ Localization in CT

Autor:	Navarro, Fernando, Sekuboyina, Anjany, Waldmannstetter, Diana, Peeken, Jan C., Combs, Stephanie E., Menze, Bjoern H.
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Robust localization of organs in computed tomography scans is a constant pre-processing requirement for organ-specific image retrieval, radiotherapy planning, and interventional image analysis. In contrast to current solutions based on exhaustive search or region proposals, which require large amounts of annotated data, we propose a deep reinforcement learning approach for organ localization in CT. In this work, an artificial agent is actively self-taught to localize organs in CT by learning from its asserts and mistakes. Within the context of reinforcement learning, we propose a novel set of actions tailored for organ localization in CT. Our method can use as a plug-and-play module for localizing any organ of interest. We evaluate the proposed solution on the public VISCERAL dataset containing CT scans with varying fields of view and multiple organs. We achieved an overall intersection over union of 0.63, an absolute median wall distance of 2.25 mm, and a median distance between centroids of 3.65 mm. Comment: Accepted paper in MIDL 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2005.04974 Zobrazit plný text záznamu View this record from Arxiv