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Multiorgan structures detection using deep convolutional neural networks
Author(s): Jorge Onieva Onieva; Germán González Serrano; Thomas P. Young; George R. Washko; María Jesús Ledesma Carbayo; Raúl San José Estépar
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Paper Abstract

Many automatic image analysis algorithms in medical imaging require a good initialization to work properly. A similar problem occurs in many imaging-based clinical workflows, which depend on anatomical landmarks. The localization of anatomic structures based on a defined context provides with a solution to that problem, which turns out to be more challenging in medical imaging where labeled images are difficult to obtain. We propose a two-stage process to detect and regress 2D bounding boxes of predefined anatomical structures based on a 2D surrounding context. First, we use a deep convolutional neural network (DCNN) architecture to detect the optimal slice where an anatomical structure is present, based on relevant landmark features. After this detection, we employ a similar architecture to perform a 2D regression with the aim of proposing a bounding box where the structure is encompassed. We trained and tested our system for 57 anatomical structures defined in axial, sagittal and coronal planes with a dataset of 504 labeled Computed Tomography (CT) scans. We compared our method with a well-known object detection algorithm (Viola Jones) and with the inter-rater error for two human experts. Despite the relatively small number of scans and the exhaustive number of structures analyzed, our method obtained promising and consistent results, which proves our architecture very generalizable to other anatomical structures.

Paper Details

Date Published: 2 March 2018
PDF: 11 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057428 (2 March 2018); doi: 10.1117/12.2293761
Show Author Affiliations
Jorge Onieva Onieva, Brigham and Women's Hospital (United States)
Germán González Serrano, Brigham and Women's Hospital (United States)
Thomas P. Young, Brigham and Women's Hospital (United States)
George R. Washko, Brigham and Women's Hospital (United States)
María Jesús Ledesma Carbayo, Univ. Politécnica de Madrid (Spain)
CIBER-BBN (Spain)
Raúl San José Estépar, Brigham and Women's Hospital (United States)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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