Auto-segmentation of thoraco-abdominal organs in free breathing pediatric dynamic MRI

• Published in: Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling Vol. 12466
• Main Authors: Akhtar, Yusuf, Udupa, Jayaram K, Tong, Yubing, Liu, Tiange, Wu, Caiyun, Odhner, Dewey, Mcdonough, Joseph M, Lott, Carina, Clark, Abbie, Anari, Jason B, Cahill, Patrick, Torigian, Drew A
• Format: Journal Article Conference Proceeding
• Language: English
• Published: United States 01.02.2023
• Subjects: auto-segmentation; Lung; natural intelligence; Thoracic Insufficiency Syndrome; dynamic magnetic resonance imaging (dMRI); artificial intelligence; deep neural networks; pediatric subjects
• ISSN: 0277-786X; 1996-756X
• DOI: 10.1117/12.2654995

Quantitative analysis of the dynamic properties of thoraco-abdominal organs such as lungs during respiration could lead to more accurate surgical planning for disorders such as Thoracic Insufficiency Syndrome (TIS). This analysis can be done from semi-automatic delineations of the aforesaid organs in scans of the thoraco-abdominal body region. Dynamic magnetic resonance imaging (dMRI) is a practical and preferred imaging modality for this application, although automatic segmentation of the organs in these images is very challenging. In this paper, we describe an auto-segmentation system we built and evaluated based on dMRI acquisitions from 95 healthy subjects. For the three recognition approaches, the system achieves a best average location error (LE) of about 1 voxel for the lungs. The standard deviation (SD) of LE is about 1-2 voxels. For the delineation approach, the average Dice coefficient (DC) is about 0.95 for the lungs. The standard deviation of DC is about 0.01 to 0.02 for the lungs. The system seems to be able to cope with the challenges posed by low resolution, motion blur, inadequate contrast, and image intensity non-standardness quite well. We are in the process of testing its effectiveness on TIS patient dMRI data and on other thoraco-abdominal organs including liver, kidneys, and spleen.