Fully automated aortic measurements via CNN-based joint segmentation and localization

Sudeep Katakol; Zhangxing Bian; Yanglong Lu; Greg Spahlinger; Charles R. Hatt; Nicholas S. Burris

doi:10.1117/12.2654749

7 April 2023 Fully automated aortic measurements via CNN-based joint segmentation and localization

Sudeep Katakol, Zhangxing Bian, Yanglong Lu, Greg Spahlinger, Charles R. Hatt, Nicholas S. Burris

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 1246514 (2023) https://doi.org/10.1117/12.2654749
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Diagnosis of thoracic aortic aneurysm typically involves measuring the diameters at various locations on the aorta from computed tomography angiograms (CTAs). Human measurement is time-consuming and suffers from inter and intra-user variability, motivating the need for automated, repeatable measurement software. This work presents a convolutional neural network (CNN)-based algorithm for fully automated aortic measurements. We employ the CNN to perform aortic segmentation and localization of key landmarks jointly, which performs better than individual models for each task. The segmentation mask and landmarks are subsequently used to obtain the centerline and cross-sectional diameters of the aorta using a combination of image processing techniques. We gather a dataset of CTAs from patients with ongoing imaging surveillance of thoracic aortic aneurysm and demonstrate the performance of our algorithm by quantitative comparisons against measurements from human raters. We observe that for most locations, the mean absolute error between human and computer-generated measurements is less than 1 mm, which is at or lower than the level of variability in human measurements. Furthermore, we showcase the behavior of our method through various visual examples, discuss its limitations and propose possible improvements.

Conference Presentation

Citation Download Citation

Sudeep Katakol, Zhangxing Bian, Yanglong Lu, Greg Spahlinger, Charles R. Hatt, and Nicholas S. Burris "Fully automated aortic measurements via CNN-based joint segmentation and localization", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 1246514 (7 April 2023); https://doi.org/10.1117/12.2654749

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