Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans

• URL: http://arxiv.org/abs/2306.03494v2
• Date: Wed, 28 May 2025 15:54:51 GMT
• Title: Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans
• Authors: Ikboljon Sobirov, Cheng Xie, Muhammad Siddique, Parijat Patel, Kenneth Chan, Thomas Halborg, Christos P. Kotanidis, Zarqaish Fatima, Henry West, Sheena Thomas, Maria Lyasheva, Donna Alexander, David Adlam, Praveen Rao, Das Indrajeet, Aparna Deshpande, Amrita Bajaj, Jonathan C L Rodrigues, Benjamin J Hudson, Vivek Srivastava, George Krasopoulos, Rana Sayeed, Qiang Zhang, Pete Tomlins, Cheerag Shirodaria, Keith M. Channon, Stefan Neubauer, Charalambos Antoniades, Mohammad Yaqub,
• Abstract summary: We introduce LegoNet, a deep learning framework that alternates CNN-based and SwinViT-based blocks to enhance feature learning for medical image segmentation.<n>These PVAT regions have been shown to possess prognostic value in assessing cardiovascular risk and primary clinical outcomes.<n>We evaluate LegoNet on large datasets, achieving superior performance to other leading architectures.
• Score: 6.400887076199784
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Since the emergence of convolutional neural networks (CNNs) and, later, vision transformers (ViTs), deep learning architectures have predominantly relied on identical block types with varying hyperparameters. We propose a novel block alternation strategy to leverage the complementary strengths of different architectural designs, assembling structurally distinct components similar to Lego blocks. We introduce LegoNet, a deep learning framework that alternates CNN-based and SwinViT-based blocks to enhance feature learning for medical image segmentation. We investigate three variations of LegoNet and apply this concept to a previously unexplored clinical problem: the segmentation of the internal mammary artery (IMA), aorta, and perivascular adipose tissue (PVAT) from computed tomography angiography (CTA) scans. These PVAT regions have been shown to possess prognostic value in assessing cardiovascular risk and primary clinical outcomes. We evaluate LegoNet on large datasets, achieving superior performance to other leading architectures. Furthermore, we assess the model's generalizability on external testing cohorts, where an expert clinician corrects the model's segmentations, achieving DSC > 0.90 across various external, international, and public cohorts. To further validate the model's clinical reliability, we perform intra- and inter-observer variability analysis, demonstrating strong agreement with human annotations. The proposed methodology has significant implications for diagnostic cardiovascular management and early prognosis, offering a robust, automated solution for vascular and perivascular segmentation and risk assessment in clinical practice, paving the way for personalised medicine.

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