Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866916763247050752 |
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| author | Sobirov, Ikboljon Xie, Cheng Siddique, Muhammad Patel, Parijat Chan, Kenneth Halborg, Thomas Kotanidis, Christos P. Fatima, Zarqaish West, Henry Thomas, Sheena Lyasheva, Maria Alexander, Donna Adlam, David Rao, Praveen Indrajeet, Das Deshpande, Aparna Bajaj, Amrita Rodrigues, Jonathan C L Hudson, Benjamin J Srivastava, Vivek Krasopoulos, George Sayeed, Rana Zhang, Qiang Tomlins, Pete Shirodaria, Cheerag Channon, Keith M. Neubauer, Stefan Antoniades, Charalambos Yaqub, Mohammad |
| author_facet | Sobirov, Ikboljon Xie, Cheng Siddique, Muhammad Patel, Parijat Chan, Kenneth Halborg, Thomas Kotanidis, Christos P. Fatima, Zarqaish West, Henry Thomas, Sheena Lyasheva, Maria Alexander, Donna Adlam, David Rao, Praveen Indrajeet, Das Deshpande, Aparna Bajaj, Amrita Rodrigues, Jonathan C L Hudson, Benjamin J Srivastava, Vivek Krasopoulos, George Sayeed, Rana Zhang, Qiang Tomlins, Pete Shirodaria, Cheerag Channon, Keith M. Neubauer, Stefan Antoniades, Charalambos Yaqub, Mohammad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_03494 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans Sobirov, Ikboljon Xie, Cheng Siddique, Muhammad Patel, Parijat Chan, Kenneth Halborg, Thomas Kotanidis, Christos P. Fatima, Zarqaish West, Henry Thomas, Sheena Lyasheva, Maria Alexander, Donna Adlam, David Rao, Praveen Indrajeet, Das Deshpande, Aparna Bajaj, Amrita Rodrigues, Jonathan C L Hudson, Benjamin J Srivastava, Vivek Krasopoulos, George Sayeed, Rana Zhang, Qiang Tomlins, Pete Shirodaria, Cheerag Channon, Keith M. Neubauer, Stefan Antoniades, Charalambos Yaqub, Mohammad Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2306.03494 |