Automatic Segmentation of Aortic and Mitral Valves for Heart Surgical Planning of Hypertrophic Obstructive Cardiomyopathy

Limin Zheng, Hongyu Chen, Lu Qing, Jian Zhuang, Bo Meng, Xiaowei Xu
Proceedings of the 15th Asian Conference on Machine Learning, PMLR 222:1715-1730, 2024.

Abstract

Hypertrophic obstructive cardiomyopathy (HOCM) is a leading cause of sudden cardiac death in young people. Septal myectomy surgery has been recognized as the gold standard for non-pharmacological therapy of HOCM, in which aortic and mitral valves are critical regions for surgical planning.Currently, manual segmentation of aortic and mitral valves is widely performed in clinical practice to construct 3D models used for HOCM surgical planning. Such a process, however, is time-consuming and costly. In this paper, we integrate anatomical prior knowledge into deep learning for automatic segmentation of aortic and mitral valves.In particular, a two-stage method is proposed: we first obtain the region of interest (RoI) from a CT image, where heart segmentation is then performed. The spatial relationship between heart substructures is utilized to identify a valve region that contains the aortic and mitral valves. Unlike typical two-stage methods, we feed the refined segmentation of the left ventricle, left atrium, and aorta as additional input for the valve segmentation. By incorporating this anatomical prior knowledge, deep neural networks (DNNs) can leverage the surrounding anatomical structures to improve valve segmentation. We collected a dataset of 27 CT images from patients with a medical history of septal myectomy surgery.Experimental results show that our method achieves an average Dice score of 71.2% and an improvement of 4.2% over existing methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v222-zheng24a, title = {Automatic Segmentation of Aortic and Mitral Valves for Heart Surgical Planning of Hypertrophic Obstructive Cardiomyopathy}, author = {Zheng, Limin and Chen, Hongyu and Qing, Lu and Zhuang, Jian and Meng, Bo and Xu, Xiaowei}, booktitle = {Proceedings of the 15th Asian Conference on Machine Learning}, pages = {1715--1730}, year = {2024}, editor = {Yanıkoğlu, Berrin and Buntine, Wray}, volume = {222}, series = {Proceedings of Machine Learning Research}, month = {11--14 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v222/zheng24a/zheng24a.pdf}, url = {https://proceedings.mlr.press/v222/zheng24a.html}, abstract = {Hypertrophic obstructive cardiomyopathy (HOCM) is a leading cause of sudden cardiac death in young people. Septal myectomy surgery has been recognized as the gold standard for non-pharmacological therapy of HOCM, in which aortic and mitral valves are critical regions for surgical planning.Currently, manual segmentation of aortic and mitral valves is widely performed in clinical practice to construct 3D models used for HOCM surgical planning. Such a process, however, is time-consuming and costly. In this paper, we integrate anatomical prior knowledge into deep learning for automatic segmentation of aortic and mitral valves.In particular, a two-stage method is proposed: we first obtain the region of interest (RoI) from a CT image, where heart segmentation is then performed. The spatial relationship between heart substructures is utilized to identify a valve region that contains the aortic and mitral valves. Unlike typical two-stage methods, we feed the refined segmentation of the left ventricle, left atrium, and aorta as additional input for the valve segmentation. By incorporating this anatomical prior knowledge, deep neural networks (DNNs) can leverage the surrounding anatomical structures to improve valve segmentation. We collected a dataset of 27 CT images from patients with a medical history of septal myectomy surgery.Experimental results show that our method achieves an average Dice score of 71.2% and an improvement of 4.2% over existing methods.} }
Endnote
%0 Conference Paper %T Automatic Segmentation of Aortic and Mitral Valves for Heart Surgical Planning of Hypertrophic Obstructive Cardiomyopathy %A Limin Zheng %A Hongyu Chen %A Lu Qing %A Jian Zhuang %A Bo Meng %A Xiaowei Xu %B Proceedings of the 15th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Berrin Yanıkoğlu %E Wray Buntine %F pmlr-v222-zheng24a %I PMLR %P 1715--1730 %U https://proceedings.mlr.press/v222/zheng24a.html %V 222 %X Hypertrophic obstructive cardiomyopathy (HOCM) is a leading cause of sudden cardiac death in young people. Septal myectomy surgery has been recognized as the gold standard for non-pharmacological therapy of HOCM, in which aortic and mitral valves are critical regions for surgical planning.Currently, manual segmentation of aortic and mitral valves is widely performed in clinical practice to construct 3D models used for HOCM surgical planning. Such a process, however, is time-consuming and costly. In this paper, we integrate anatomical prior knowledge into deep learning for automatic segmentation of aortic and mitral valves.In particular, a two-stage method is proposed: we first obtain the region of interest (RoI) from a CT image, where heart segmentation is then performed. The spatial relationship between heart substructures is utilized to identify a valve region that contains the aortic and mitral valves. Unlike typical two-stage methods, we feed the refined segmentation of the left ventricle, left atrium, and aorta as additional input for the valve segmentation. By incorporating this anatomical prior knowledge, deep neural networks (DNNs) can leverage the surrounding anatomical structures to improve valve segmentation. We collected a dataset of 27 CT images from patients with a medical history of septal myectomy surgery.Experimental results show that our method achieves an average Dice score of 71.2% and an improvement of 4.2% over existing methods.
APA
Zheng, L., Chen, H., Qing, L., Zhuang, J., Meng, B. & Xu, X.. (2024). Automatic Segmentation of Aortic and Mitral Valves for Heart Surgical Planning of Hypertrophic Obstructive Cardiomyopathy. Proceedings of the 15th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 222:1715-1730 Available from https://proceedings.mlr.press/v222/zheng24a.html.

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