Segmentation of male pelvic organs on computed tomography with a deep neural network fine-tuned by a level-set method

• Type: Article in International Scientific Journal
• Date: 2022-01

Authors

Gonçalo Almeida (Author) Other; Ana Rita Figueira (Author) Other; Joana Lencart (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Computers in Biology and Medicine

Vol. 140

Pages: 1-8

ISSN: 0010-4825

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

ISI Web of Science

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Scientific classification

CORDIS: Technological sciences

FOS: Medical and Health sciences

Other information

Authenticus ID: P-00V-VCH

DOI: 10.1016/j.compbiomed.2021.105107

Abstract (EN): Computed Tomography (CT) imaging is used in Radiation Therapy planning, where the treatment is carefully tailored to each patient in order to maximize radiation dose to the target while decreasing adverse effects to nearby healthy tissues. A crucial step in this process is manual organ contouring, which if performed automatically could considerably decrease the time to starting treatment and improve outcomes. Computerized segmentation of male pelvic organs has been studied for decades and deep learning models have brought considerable advances to the field, but improvements are still demanded. A two-step framework for automatic segmentation of the prostate, bladder and rectum is presented: a convolutional neural network enhanced with attention gates performs an initial segmentation, followed by a region-based active contour model to fine-tune the segmentations to each patient's specific anatomy. The framework was evaluated on a large collection of planning CTs of patients who had Radiation Therapy for prostate cancer. The Surface Dice Coefficient improved from 79.41 to 81.00% on segmentation of the prostate, 94.03-95.36% on the bladder and 82.17-83.68% on the rectum, comparing the proposed framework with the baseline convolutional neural network. This study shows that traditional image segmentation algorithms can help improve the immense gains that deep learning models have brought to the medical imaging segmentation field.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8