An attention residual u-net with differential preprocessing and geometric postprocessing: Learning how to segment vasculature including intracranial aneurysms

Mu, Nan; Lyu, Zonghan; Rezaeitaleshmahalleh, Mostafa; Tang, Jinshan; Jiang, Jingfeng

doi:10.1016/j.media.2022.102697

Cited by 45 publications

(18 citation statements)

References 30 publications

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“…Hong and Sheikh [36] presented a deep belief network (DBN) for detecting and segmenting the preoperative AAA region of 2D CTA. As one of the U-Net variants, our previously proposed ARU-Net [26] has multiple advantages compared to other methods: (1) the depthaware attention gates are grid-based gating, which ensures that the attention coefficient focus on local areas to preserve small secondary blood vessels; (2) dense label prediction maintains a large amount of detailed knowledge and location information; (3) the simplicity of ARU-Net's structure maintains a relatively low computational cost. However, despite its excellent focal information learning, global information extraction is the one weakness of the ARU-Net.…”

Section: Deep Learning-based Image Segmentation Methodsmentioning

confidence: 99%

“…Motivated by the unmet need, our group proposed an innovative deep-learning neural network model named Attention-based residual U-Net (ARU-Net) [26], the first deep-learning-based image segmentation method tested for computational hemodynamics in cerebral aneurysms. We recently developed a Context-Aware Cascaded U-Net (CACU-Net) to classify AAAs' lumen and intraluminal thrombosis [20].…”

Section: Introductionmentioning

confidence: 99%

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Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study

Lyu,

King,

Rezaeitaleshmahalleh

et al. 2023

Biomed. Phys. Eng. Express

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Computational hemodynamics is increasingly used to quantify hemodynamic characteristics in and around abdominal aortic aneurysms (AAA) in a patient-specific fashion. However, the time-consuming manual annotation hinders the clinical translation of computational hemodynamic analysis. Thus, we investigate the feasibility of using deep-learning-based image segmentation methods to reduce the time required for manual segmentation.Two of the latest deep-learning-based image segmentation methods, ARU-Net and CACU-Net, were used to test the feasibility of automated computer model creation for computational hemodynamic analysis. Morphological features and hemodynamic metrics of 30 computed tomography angiography (CTA) scans were compared between pre-dictions and manual models.The DICE score for both networks was 0.916, and the correlation value was above 0.95, indicating their ability to generate models comparable to human segmentation. The Bland-Altman analysis shows a good agreement between deep learning and manual segmentation results. Compared with manual (computational hemodynamics) model recreation, the time for automated computer model generation was significantly reduced (from ~2hr to ~10min).Automated image segmentation can significantly reduce time expenses on the recreation of patient-specific AAA models. Moreover, our study showed that both CACU-Net and ARU-Net could accomplish AAA segmentation, and CACU-Net outperformed ARU-Net in terms of accuracy and time-saving.

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Section: Deep Learning-based Image Segmentation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study

Lyu,

King,

Rezaeitaleshmahalleh

et al. 2023

Biomed. Phys. Eng. Express

Self Cite

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“…Zhao et al [23] integrate a feature pyramid with a U-Net++ model to segment coronary arteries in ICAs, and proposed a compound loss function that contains Dice loss and dilated Dice loss. Mu et al [24] introduced the long skip connections of the attention gate at each layer to emphasize the field of view (FOV) for IAs and embed residual-based short skip connections in each layer to implement in-depth supervision to help the network converge.…”

Section: Medical Image Segmentationmentioning

confidence: 99%

LPE-Unet: An Improved UNet Network Based on Perceptual Enhancement

2023

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In Computed Tomography (CT) images of the coronary arteries, the segmentation of calcified plaques is extremely important for the examination, diagnosis, and treatment of coronary heart disease. However, one characteristic of the lesion is that it has a small size, which brings two difficulties. One is the class imbalance when computing loss function and the other is that small-scale targets are prone to losing details in the continuous downsampling process, and the blurred boundary makes the segmentation accuracy less satisfactory. Therefore, the segmentation of calcified plaques is a very challenging task. To address the above problems, in this paper, we design a framework named LPE-UNet, which adopts an encoder–decoder structure similar to UNet. The framework includes two powerful modules named the low-rank perception enhancement module and the noise filtering module. The low-rank perception enhancement module extracts multi-scale context features by increasing the receptive field size to aid target detection and then uses an attention mechanism to filter out redundant features. The noise filtering module suppresses noise interference in shallow features to high-level features in the process of multi-scale feature fusion. It computes a pixel-wise weight map of low-level features and filters out useless and harmful information. To alleviate the problem of class imbalance caused by small-sized lesions, we use a weighted cross-entropy loss function and Dice loss to perform mixed supervised training on the network. The proposed method was evaluated on the calcified plaque segmentation dataset, achieving a high F1 score of 0.941, IoU of 0.895, and Dice of 0.944. This result verifies the effectiveness and superiority of our approach for accurately segmenting calcified plaques. As there is currently no authoritative publicly available calcified plaque segmentation dataset, we have constructed a new dataset for coronary artery calcified plaque segmentation (Calcified Plaque Segmentation Dataset, CPS Dataset).

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“…Simultaneously, deep learning approaches have evolved in both the clinical and pre-clinical non-lung tissue segmentation space. This includes use of 3D convolutional models for segmentation of skeletal muscle in murine hind-limbs [29] , blood vessels in human liver [30] , and structures of human eyes, where custom deep learning models have been developed to identify extraocular muscles and optic nerves [31] , retinal vessels [32] , and even arteries related to intracranial aneurysms [33] . Such work demonstrates the widespread success of deep learning in accelerating the work of the medical imaging community.…”

Section: Introductionmentioning

confidence: 99%

Applying deep learning to segmentation of murine lung tumors in pre-clinical micro-computed tomography

Montgomery,

Duan,

Manzuk

et al. 2024

Translational Oncology

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An attention residual u-net with differential preprocessing and geometric postprocessing: Learning how to segment vasculature including intracranial aneurysms

Cited by 45 publications

References 30 publications

Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study

Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study

LPE-Unet: An Improved UNet Network Based on Perceptual Enhancement

Applying deep learning to segmentation of murine lung tumors in pre-clinical micro-computed tomography

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