Automatic Left Ventricular Cavity Segmentation via Deep Spatial Sequential Network in 4D Computed Tomography Studies

• URL: http://arxiv.org/abs/2412.12853v1
• Date: Tue, 17 Dec 2024 12:29:32 GMT
• Title: Automatic Left Ventricular Cavity Segmentation via Deep Spatial Sequential Network in 4D Computed Tomography Studies
• Authors: Yuyu Guo, Lei Bi, Zhengbin Zhu, David Dagan Feng, Ruiyan Zhang, Qian Wang, Jinman Kim,
• Abstract summary: We propose a new method to automatically segment temporal cardiac images. We introduce a spatial sequential (SS) network to learn the deformation and motion characteristics of the LVC in an unsupervised manner. Our results on a cardiac computed tomography (CT) dataset demonstrated that our network with bi-directional learning (SS-BL) method outperformed existing methods for LVC segmentation.
• Score: 20.358413194053103
• License:
• Abstract: Automated segmentation of left ventricular cavity (LVC) in temporal cardiac image sequences (multiple time points) is a fundamental requirement for quantitative analysis of its structural and functional changes. Deep learning based methods for the segmentation of LVC are the state of the art; however, these methods are generally formulated to work on single time points, and fails to exploit the complementary information from the temporal image sequences that can aid in segmentation accuracy and consistency among the images across the time points. Furthermore, these segmentation methods perform poorly in segmenting the end-systole (ES) phase images, where the left ventricle deforms to the smallest irregular shape, and the boundary between the blood chamber and myocardium becomes inconspicuous. To overcome these limitations, we propose a new method to automatically segment temporal cardiac images where we introduce a spatial sequential (SS) network to learn the deformation and motion characteristics of the LVC in an unsupervised manner; these characteristics were then integrated with sequential context information derived from bi-directional learning (BL) where both chronological and reverse-chronological directions of the image sequence were used. Our experimental results on a cardiac computed tomography (CT) dataset demonstrated that our spatial-sequential network with bi-directional learning (SS-BL) method outperformed existing methods for LVC segmentation. Our method was also applied to MRI cardiac dataset and the results demonstrated the generalizability of our method.

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