Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends

Qureshi, Imran; Yan, Jun; Abbas, Qaisar; Shaheed, Kashif; Riaz, Awais Bin; Wahid, Abdul; Khan, Muhammad Waseem Jan; Szczuko, Piotr

doi:10.1016/j.inffus.2022.09.031

Cited by 109 publications

(28 citation statements)

References 111 publications

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“…MIoU (%) (3,6,12) 96.14 (6,12,18) 96.13 (12,18,24) 96.14 (3,6,12,18) 96.34 respectively, in comparison with the circumstances where only the original or improved UCTransNet is applied. Consequently, the addition of the enhancement network (Improved PRIDNet) can effectively improve the detail and global information within the target region of the input image, further improving the performance of the segmentation network.…”

Section: Dilated Convolution Structurementioning

confidence: 99%

“…Over an extensive period, scholars have dedicated their efforts to the advancement of computer-aided systems designed to support clinical practice. Previous studies [6][7][8] involve a comprehensive review of the application of machine learning techniques in the domain of ultrasound medical imaging. Concerning the extraction of target regions from ultrasound images, the application of image segmentation methods facilitates the automated extraction of the designated regions of interest.…”

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confidence: 99%

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A target region extraction method for ultrasound medical images based on improvedPRIDNetandUCTransNet

Zhai,

Tian,

et al. 2024

Int J Imaging Syst Tech

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Computer‐aided diagnosis is pivotal in augmenting the diagnostic efficiency of ultrasound images. Nonetheless, the substantial presence of noise and artifacts in ultrasound images presents a challenge to the precise segmentation of the target region. To highlight and analyze the diagnostic information such as tissues, organs, and lesions in ultrasound medical images more accurately, a target region extraction method combining the improved PRIDNet and UCTransNet for ultrasound medical images is proposed. The method sequentially enhances and segments the target region of the image, thereby solving the problem of inconspicuous target region caused by noise and artifacts in ultrasound images. There are three key characteristics: (i) The feature extraction part of the enhancement network (improved PRIDNet) is redesigned for speckle noise to improve the network's ability to extract information and highlight the feature information of the target region in ultrasound images. (ii) The segmentation network with the addition of the underlying information on UCTransNet would effectively improves Channel‐wise Cross fusion Transformer (CCT) and decoder feature fusion capability. (iii) By combining the enhancement network with the segmentation network, we can further improve the segmentation accuracy of the target region in the presence of noise interference. The experiments conducted on both UFSU that we prepared and on some public datasets including BUSI, FHC, and CT2US have demonstrated that The proposed method attains MIoU, DSC, Acc, and HD of 96.34%, 98.12%, 99.35%, and 8.64 in CT2US, respectively. The method significantly surpasses those certain state‐of‐the‐art methods, demonstrating its potential to offer valuable guidance for clinical treatment. The code will be publicly released at https://github.com/425877/Target-Region-Extraction-Method-for-Ultrasound-Medical-Images.

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Section: Dilated Convolution Structurementioning

confidence: 99%

mentioning

confidence: 99%

A target region extraction method for ultrasound medical images based on improvedPRIDNetandUCTransNet

Zhai,

Tian,

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

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“…Among them, image segmentation is the main problem in machine vision research. The so-called image segmentation refers to the division of an image into several disjoint regions based on features such as grayscale, color, spatial texture, geometry, etc., so that these features show consistency or similarity within the same region and distinct differences between different regions [17]. Existing studies have reached a consensus on the general laws of image segmentation.…”

Section: Photocatalytic Dye Degradationmentioning

confidence: 99%

Adaptive analysis of rapid prediction of coal quality information based on image analysis

Liu

Zhang

2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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Machine vision technology has shown great potential for development and application in the coal heat utilization and coal chemical production process It is important to carry out image analysis-based stability adaptation analysis of coal heat utilization and coal chemical equipment. To further expand its application, this work reviewed the characteristics of different vision sensor systems and image processing algorithms in the process of image acquisition and processing, conducted applicability analysis for rapid prediction of coal quality, presented the current problems and future development trends of image analysis technology in the coal heat utilization and coal chemical production process. It provided theoretical guidance and reference for promoting automation, intelligence, and mechanization in the coal industry.

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“…To overcome the challenge of limited, well-annotated training data in developing deep learning techniques for medical image segmentation, a number of semi-supervised and weakly supervised algorithms have been proposed Qureshi et al, 2023). While semi-supervised strategies still leverage a small number of refined image labels through data augmentation, transfer learning, and interactive segmentation to enhance the accuracy and capacity of pre-trained DL models, weakly supervised techniques rely entirely on coarse labels in the formats of bounding boxes (Rajchl et al, 2017), scribbles ), points (Roth et al, 2021, or even categorical labels (Lin et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Weakly Supervised Intracranial Hemorrhage Segmentation Using Hierarchical Combination of Attention Maps from a Swin Transformer

Rasoulian

Salari

Xiao

2022

Lecture Notes in Computer Science

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Intracranial hemorrhage (ICH) is a life-threatening medical emergency caused by various factors. Timely and precise diagnosis of ICH is crucial for administering effective treatment and improving patient survival rates. While deep learning techniques have emerged as the leading approach for medical image analysis and processing, the most commonly employed supervised learning often requires large, high-quality annotated datasets that can be costly to obtain, particularly for pixel/voxel-wise image segmentation. To address this challenge and facilitate ICH treatment decisions, we proposed a novel weakly supervised ICH segmentation method that leverages a hierarchical combination of head-wise gradient-infused self-attention maps obtained from a Swin transformer. The transformer is trained using an ICH classification task with categorical labels. To build and validate the proposed technique, we used two publicly available clinical CT datasets, namely RSNA 2019 Brain CT hemorrhage and PhysioNet. Additionally, we conducted an exploratory study comparing two learning strategies -binary classification and full ICH subtyping -to assess their impact on self-attention and our weakly supervised ICH segmentation framework. The proposed algorithm was compared against the popular U-Net with full supervision, as well as a similar weakly supervised approach using Grad-CAM for ICH segmentation. With a mean Dice score of 0.47, our technique achieved similar ICH segmentation performance as the U-Net and outperformed the Grad-CAM based approach, demonstrating the excellent potential of the proposed framework in challenging medical image segmentation tasks.

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Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends

Cited by 109 publications

References 111 publications

A target region extraction method for ultrasound medical images based on improvedPRIDNetandUCTransNet

A target region extraction method for ultrasound medical images based on improvedPRIDNetandUCTransNet

Adaptive analysis of rapid prediction of coal quality information based on image analysis

Weakly Supervised Intracranial Hemorrhage Segmentation Using Hierarchical Combination of Attention Maps from a Swin Transformer

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