AB-ResUNet+: Improving Multiple Cardiovascular Structure Segmentation from Computed Tomography Angiography Images

Habijan, Marija; Galić, Irena; Romić, Krešimir; Leventić, Hrvoje

doi:10.3390/app12063024

Cited by 7 publications

(6 citation statements)

References 55 publications

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“…The deep neural network of the model reduces percent error and allows higher image classification accuracy. ResUNet++ has been used to successfully segment lungs in chest X-ray images with a small data set and high accuracy, as well as distinguish different cardiovascular structures [24,25]. Compared to the UNet models used in our study, ResUNet++ has similar validation accuracy, but requires a much longer training time and more memory.…”

Section: Comparison With Similar Studies On Image Segmentationmentioning

confidence: 96%

Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks

Campbell,

Yadav,

Hussein

et al. 2023

Applied Sciences

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Phase contrast (PC) magnetic resonance imaging (MRI) is a primary method used to quantify blood flow. Cerebral blood flow (CBF) is an important hemodynamic parameter to characterize cerebrovascular and neurological diseases. However, a critical step in CBF quantification using PC MRI is vessel segmentation, which is largely manual, and thus time-consuming and prone to interrater variability. Here, we present encoder–decoder deep learning models to automate segmentation of neck arteries to accurately quantify CBF. The PC-MRI data were collected from 46 Moyamoya (MM) patients and 107 healthy control (HC) participants. Three segmentation U-Net models (Standard, Nested, and Attention) were compared. The PC MRI images were taken before and 15 min after vasodilation. The models were assessed based on their ability to detect the internal carotid arteries (ICAs), external carotid arteries (ECAs), and vertebral arteries (VAs), using the Dice score coefficient (DSC) of overlap between manual and predicted segmentations and receiver operator characteristic (ROC) metric. Analysis of variance, Wilcoxon rank-sum test, and paired t-test were used for comparisons. The Standard U-NET, Attention U-Net, and Nest U-Net models achieved results of mean DSCs of 0.81 ± 0.21, and 0.85 ± 0.14, and 0.85 ± 0.13, respectively. The ROC curves revealed high area under the curve scores for all methods (≥0.95). While the Nested and Attention U-Net architectures accomplished reliable segmentation performance for HC and MM subsets, Standard U-Net did not perform as well in the subset of MM patients. Blood flow velocities calculated by the models were statistically comparable. In conclusion, optimized deep learning architectures can successfully segment neck arteries in PC MRI images and provide precise quantification of their blood flow.

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Section: Comparison With Similar Studies On Image Segmentationmentioning

confidence: 96%

Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks

Campbell,

Yadav,

Hussein

et al. 2023

Applied Sciences

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show abstract

“…Examples include the segmentation of brain tumors in MRI [152], lung nodules in chest CT scans [153], polyps [154], and vessel delineation [155]. Additionally, they find widespread use in cardiovascular image segmentation tasks, encompassing the isolation of specific structures like the aorta [156,157], heart chambers [158][159][160], epicardial tissue [161], left atrial appendage [162,163], and coronary arteries [164]. Precise segmentation is invaluable as it facilitates quantification, classification, and visualization of medical image data, ultimately supporting more informed clinical decision-making processes.…”

Section: Image Segmentationmentioning

confidence: 99%

“…Beyond clinical diagnosis and treatment, cardiac imaging plays a pivotal role in advancing cardiovascular research. It enables scientists to explore cardiac functions, blood flow dynamics [211], and the interaction between different heart chambers [158][159][160]. This deeper understanding of cardiac physiology and pathology helps researchers uncover the mechanisms behind heart-related conditions and develop innovative treatment approaches.…”

Section: Cardiac Imagingmentioning

confidence: 99%

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

Galić,

Habijan,

Leventić

et al. 2023

Electronics

Self Cite

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Artificial intelligence (AI) advancements, especially deep learning, have significantly improved medical image processing and analysis in various tasks such as disease detection, classification, and anatomical structure segmentation. This work overviews fundamental concepts, state-of-the-art models, and publicly available datasets in the field of medical imaging. First, we introduce the types of learning problems commonly employed in medical image processing and then proceed to present an overview of commonly used deep learning methods, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), with a focus on the image analysis task they are solving, including image classification, object detection/localization, segmentation, generation, and registration. Further, we highlight studies conducted in various application areas, encompassing neurology, brain imaging, retinal analysis, pulmonary imaging, digital pathology, breast imaging, cardiac imaging, bone analysis, abdominal imaging, and musculoskeletal imaging. The strengths and limitations of each method are carefully examined, and the paper identifies pertinent challenges that still require attention, such as the limited availability of annotated data, variability in medical images, and the interpretability issues. Finally, we discuss future research directions with a particular focus on developing explainable deep learning methods and integrating multi-modal data.

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“…This network structure can reduce computational complexity while maintaining network performance. Inception-ResNet-A block was modified by combining one of the blocks of the traditional Inception-ResNet architecture [35] with the ResNet model [36]. The ResNet and Inception blocks can be used in combination because the ResNet module consists of residual connections that can be used to train a deep neural network, while the inception block can handle more information from input images.…”

Section: Proposed Architecture 41 Overall Architecture Of the Propose...mentioning

confidence: 99%

“…The ResNet and Inception blocks can be used in combination because the ResNet module consists of residual connections that can be used to train a deep neural network, while the inception block can handle more information from input images. Generally, the architectures of several ver- Inception-ResNet-A block was modified by combining one of the blocks of the traditional Inception-ResNet architecture [35] with the ResNet model [36]. The ResNet and Inception blocks can be used in combination because the ResNet module consists of residual connections that can be used to train a deep neural network, while the inception block can handle more information from input images.…”

Section: Proposed Architecture 41 Overall Architecture Of the Propose...mentioning

confidence: 99%

An Improved Method of Polyp Detection Using Custom YOLOv4-Tiny

et al. 2022

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Automatic detection of Wireless Endoscopic Images can avoid dangerous possible diseases such as cancers. Therefore, a number of articles have been published on different methods to enhance the speed of detection and accuracy. We also present a custom version of the YOLOv4-tiny for Wireless Endoscopic Image detection and localization that uses a You Only Look Once (YOLO) version to enhance the model accuracy. We modified the YOLOv4-tiny model by replacing the CSPDarknet-53-tiny backbone structure with the Inception-ResNet-A block to enhance the accuracy of the original YOLOv4-tiny. In addition, we implemented a new custom data augmentation method to enhance the data quality, even for small datasets. We focused on maintaining the color of medical images because the sensitivity of medical images can affect the efficiency of the model. Experimental results showed that our proposed method obtains 99.4% training accuracy; compared with the previous models, this is more than a 1.2% increase. An original model used for both detection and the segmentation of medical images may cause a high error rate. In contrast, our proposed model could eliminate the error rate of the detection and localization of disease areas from wireless endoscopic images.

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AB-ResUNet+: Improving Multiple Cardiovascular Structure Segmentation from Computed Tomography Angiography Images

Cited by 7 publications

References 55 publications

Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks

Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

An Improved Method of Polyp Detection Using Custom YOLOv4-Tiny

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