Deep Reinforcement Learning for Organ Localization in CT

Fernando Navarro, Anjany Sekuboyina, Diana Waldmannstetter, Jan C. Peeken, Stephanie E. Combs, Bjoern H. Menze

Publikation: Beitrag in FachzeitschriftKonferenzartikelBegutachtung

27 Zitate (Scopus)

Abstract

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.

OriginalspracheEnglisch
Seiten (von - bis)544-554
Seitenumfang11
FachzeitschriftProceedings of Machine Learning Research
Jahrgang121
PublikationsstatusVeröffentlicht - 2020
Veranstaltung3rd Conference on Medical Imaging with Deep Learning, MIDL 2020 - Virtual, Online, Kanada
Dauer: 6 Juli 20208 Juli 2020

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