Coronary Vessel Segmentation by Coarse-to-Fine Strategy Using U-nets

Thuy, Le Nhi Lam; Dat, Trinh Tan; Anh, Le Hoang; Kim, Jin Young; Huynh,

doi:10.1155/2021/5548517

Cited by 8 publications

(12 citation statements)

References 20 publications

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“…It also introduces too many additional parameters. [13] modifies the vessel segmentation task to a three-class classification problem between large vessels, small vessels, and background regions to reduce the problem of intra-class variation. Though these strategies may be beneficial, non-adaptive extraction methods cannot thoroughly handle multi-scale vessels.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Deep Information and Adaptive Attention Mechanism Based Coronary Reconstruction of Superior Mesenteric Artery

et al. 2023

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Vascular images contain a lot of key information, such as length, diameter and distribution. Thus reconstruction of vessels such as the Superior Mesenteric Artery is critical for the diagnosis of some abdominal diseases. However automatic segmentation of abdominal vessels is extremely challenging due to the multi-scale nature of vessels, boundary-blurring, low contrast, artifact disturbance and vascular cracks in Maximum Intensity Projection images. In this work, we propose a dual attention guided method where an adaptive adjustment field is applied to deal with multi-scale vessel information, and a channel feature fusion module is used to refine the extraction of thin vessels, reducing the interference and background noise. In particular, we propose a novel structure that accepts multiple sequential images as input, and successfully introduces spatial-temporal features by contextual information. A further IterUnet step is introduced to connect tiny cracks caused using CT scans. Comparing our proposed model with other state-of-the-art models, our model yields better segmentation and achieves an average F1 metric of 0.812.

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Section: Introductionmentioning

confidence: 99%

Multi-Scale Deep Information and Adaptive Attention Mechanism Based Coronary Reconstruction of Superior Mesenteric Artery

et al. 2023

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“…The same reason holds for the pulmonary FoV; (iii) All UNet classes had four to five layers, expect sUNet that had up to a maximum of 13 layers [42][43][44][45][46][47][48][49][50][51][52][53][54]. Note that as the number of layers increase, the DL system becomes more complex; (iv) Cross-Entropy (CE) loss function was most common or popular in all the five types of UNet , while, Dice loss function was also part of cUNet and sUNet classes ; (v) sUNet and acUNet were the two sets of classes which embraced multicenter studies [49,.…”

Section: B Five Types Of Unet and Their Attributesmentioning

confidence: 99%

“…To begin with, the encoder is the most adapted and most changeable component of the UNet architecture. Since it is practically not possible to study each of the architectural variations in the encoder, we have therefore listed here the 23 variations (E1 to E23, representing encoder changes) along with their references in a tabular format and it is as follows: (E1) conventional system (Ronneberger) [43][44][45][46][47][48][49][50][51][52]90]; (E2) cascade of convolutions [77,91,99,116,117]; (E3) parallel convolutions (multiple convolution network) [57]; (E4) convolution with dropout [70,76,86,95,101,102,134,138]; (E5) Residual network [76,78,105,129,135,138,[149][150][151]; (E6) Xception encoder [56,88,112]; (E7) encoder layers with independent inputs [104,140]; (E8) squeeze excitation (SE) network [92,103,138]; (E9) pooling types (max pooling, global average pooling) [95]; (E10) input image dimension change with changing filter (channe...…”

Section: A Encoder Variationsmentioning

confidence: 99%

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Suri¹,

Bhagawati

Agarwal³

et al. 2023

IEEE Access

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Background & Motivation: Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast. Conventional methods lack accurate and automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This is the first review of its kind that microscopically addressed UNet types by complexity, stratification of UNet by its components, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet-based architecture, and finally interfacing the three facets of AI, the pruning, the explainable AI (XAI), and the AI-bias. Methods: PRISMA was used to select 267 UNet-based studies. Five classes were identified and labeled as conventional UNet, superior UNet, attention-channel UNet, hybrid UNet, and ensemble UNet. We discovered 81 variations of UNet by considering six kinds of components, namely encoder, decoder, skip connection, bridge network, loss function, and their combination. Vascular vs. non-vascular UNet architecture was compared. AP(ai)Bias 2.0-UNet was identified in these UNet classes based on (i) attributes of UNet architecture and its performance, (ii) explainable AI (XAI), and, (iii) pruning (compression). Five bias methods such as (i) ranking, (ii) radial, (iii) regional area, (iv) PROBAST, and (v) ROBINS-I were applied and compared using a Venn diagram. Findings and Conclusions: Vascular and non-vascular UNet systems dominated with sUNet classes with attention. The majority of the studies suffered from low interest in XAI and pruning strategies. None of the UNet models qualified to be bias-free. There is a need to move from paper-to-practice paradigms for clinical evaluation and settings. INDEX TERMS Image segmentation; vascular; non-vascular; UNet classes; UNet variations; UNetcomponents; explainable AI; pruning; bias.

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“…Such behavior is shown in Figure 5a-d. 60,62,64,67,69,71] epochs, 18% [24,[54][55][56][57][58][59][60][61][62]66,67,71] optimization, and 18% [45,46,[49][50][51]53,54,58,59,61,62,[66][67][68]71,73] augmentation. The statistical distributions and analysis of the selected studies is demonstrated in Figures 4 and 5.…”

Section: Statistical Distribution Analysismentioning

confidence: 99%

“…The statistical distribution in Figure 5 details the parameters, namely (a) the attributes of parameter optimization, (b) the architectural design followed in the DL-based paradigm, (c) various performance metrics utilized in the CAD segmentation of IVUS scan, and (d) different variants of UNet adopted for CAD segmentation. In Figure 5a, the DL systems were also analyzed by considering the percentage distribution in parameter optimization, including 22% [24,47,48,51,[53][54][55][56][58][59][60][61][62]64,[67][68][69]71] learning rate, 20% [24,[46][47][48]50,51,[53][54][55][56]58,59,61,62,67,69,71] batch size, 22% [24,[45][46][47][48]50,51,[53][54][55]…”

Section: Statistical Distribution Analysismentioning

confidence: 99%

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

Kumari,

Kumar,

Kumar K

et al. 2023

JCDD

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Background and Motivation: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. Methods: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. Findings and Conclusions: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.

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Coronary Vessel Segmentation by Coarse-to-Fine Strategy Using U-nets

Cited by 8 publications

References 20 publications

Multi-Scale Deep Information and Adaptive Attention Mechanism Based Coronary Reconstruction of Superior Mesenteric Artery

Multi-Scale Deep Information and Adaptive Attention Mechanism Based Coronary Reconstruction of Superior Mesenteric Artery

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

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