Auto-segmentation of thoracic brachial plexuses for radiation therapy planning

Ademola E. Ilesanmi; Jayaram K. Udupa; Yubing Tong; Tiange Liu; Dewey Odhner; Gargi Pednekar; Sanghita Nag; Sharon Lewis; Joe Camaratta; Steve Owens; Drew A. Torigian

doi:10.1117/12.2655159

3 April 2023 Auto-segmentation of thoracic brachial plexuses for radiation therapy planning

Ademola E. Ilesanmi, Jayaram K. Udupa, Yubing Tong, Tiange Liu, Dewey Odhner, Gargi Pednekar, Sanghita Nag, Sharon Lewis, Joe Camaratta, Steve Owens, Drew A. Torigian

Author Affiliations +

Proceedings Volume 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling; 1246626 (2023) https://doi.org/10.1117/12.2655159
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Recently, deep learning networks have achieved considerable success in segmenting organs in medical images. Several methods have used volumetric information with deep networks to achieve segmentation accuracy. However, these networks suffer from interference, risk of overfitting, and low accuracy as a result of artifacts, in the case of very challenging objects like the brachial plexuses. In this paper, to address these issues, we synergize the strengths of high-level human knowledge (i.e., Natural Intelligence (NI)) with deep learning (i.e., Artificial Intelligence (AI)) for recognition and delineation of the thoracic Brachial Plexuses (BPs) in Computed Tomography (CT) images. We formulate an anatomy-guided deep learning hybrid intelligence approach for segmenting thoracic right and left brachial plexuses consisting of two key stages. In the first stage (AAR-R), objects are recognized based on a previously created fuzzy anatomy model of the body region with its key organs relevant for the task at hand wherein high-level human anatomic knowledge is precisely codified. The second stage (DL-D) uses information from AAR-R to limit the search region to just where each object is most likely to reside and performs encoder-decoder delineation in slices. The proposed method is tested on a dataset that consists of 125 images of the thorax acquired for radiation therapy planning of tumors in the thorax and achieves a Dice coefficient of 0.659.

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Ademola E. Ilesanmi, Jayaram K. Udupa, Yubing Tong, Tiange Liu, Dewey Odhner, Gargi Pednekar, Sanghita Nag, Sharon Lewis, Joe Camaratta, Steve Owens, and Drew A. Torigian "Auto-segmentation of thoracic brachial plexuses for radiation therapy planning", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 1246626 (3 April 2023); https://doi.org/10.1117/12.2655159

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KEYWORDS

Image segmentation

Anatomy

Computed tomography

Deep learning

Artificial intelligence

Object recognition

Radiotherapy

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