Efficient active contour model for medical image segmentation and correction based on edge and region information

Yang, Yunyun; Hou, Xiaoyan; Ren, Huilin

doi:10.1016/j.eswa.2021.116436

Cited by 33 publications

(12 citation statements)

References 41 publications

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“…From the perspective of theoretical techniques involved in segmentation schemes, coronary artery segmentation methods can be divided into clustering-based methods ( 10 ), region growing-based methods ( 11 ), active contour model-based methods ( 12 ), tracking-based methods ( 13 ), and specific theoretical methods ( 14 ). Gharleghi et al have provided a general review on coronary artery segmentation ( 15 ).…”

Section: Introductionmentioning

confidence: 99%

Automatic 3D coronary artery segmentation based on local region active contour model

Chen,

Jiang,

Zhang

2024

J Thorac Dis

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Background Segmentation of coronary arteries in computed tomography angiography (CTA) images plays a key role in the diagnosis and treatment of coronary-related diseases. However, manually analyzing the large amount of data is time-consuming, and interpreting this data requires the prior knowledge and expertise of radiologists. Therefore, an automatic method is needed to separate coronary arteries from a given CTA dataset. Methods Firstly, an anisotropic diffusion filter was employed to smooth the noise while preserving the vessel boundaries. The coronary skeleton was then extracted using a two-step process based on the intensity of the coronary. In the first step, the thick vessel skeleton was extracted by clustering, improved vesselness filtering and region growing, while in the second step, the thin vessel skeleton was extracted by the height ridge traversal method guided by the cylindrical model. Next, the vesselness measure, representing vessel a priori information, was incorporated into the local region active contour model based on the vessel geometry. Finally, the initial contour of the active contour model was generated using the coronary artery skeleton for effective segmentation of the three-dimensional (3D) coronary arteries. Results Experimental results on chest CTA images show that the method is able to segment coronary arteries effectively with an average precision, recall and dice similarity coefficient (DSC) of 86.64%, 91.26% and 79.13%, respectively, and has a good performance in thin vessel extraction. Conclusions The method does not require manual selection of vessel seeds or setting of initial contours, and allows for the extraction of a successful coronary artery skeleton and eventual effective segmentation of the coronary arteries.

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Section: Introductionmentioning

confidence: 99%

Automatic 3D coronary artery segmentation based on local region active contour model

Chen,

Jiang,

Zhang

2024

J Thorac Dis

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“…With the rapid development of deep learning technology, deep learning models have been widely used in image segmentation tasks [4][5][6][7][8][9][10][11][12][13], which also makes it possible to automatically and accurately segment breast tubules in H&E stained images. Nowadays, more and more deep learning models have shown great performance in the field of image segmentation.…”

Section: Introductionmentioning

confidence: 99%

UJAT-Net: A U-Net Combined Joint-Attention and Transformer for Breast Tubule Segmentation in H&E Stained Images

Liu,

Huang,

et al. 2024

IEEE Access

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The formation of breast tubules is an important evaluation index in the pathological grading of breast cancer. However, the tubules of breast present a wide variety of morphologies and a significant demonstrated significant advantages in the automatic analysis of histopathology images. We propose a Joint Attention and Transformer U-Net network to accurately segment breast tubules, named UJAT-Net. UJAT-Net uses the Joint Attention Block (named JA BLOCK) as the encoder of the network to enhance the extraction effect of the network for different layer features. And the Channel Cross fusion with Transformer (named CCT) module is used as the skip connection structure of the network. Furthermore, we employ a Transpose Cross Attention (named TCA) module as the decoder of the network to fuse the features of the skip connection layer and the decoder. Experimental results on our own invasive breast cancer tubule (Tubule of Breast Cancer, TBC) dataset and the benchmark dataset (Glas) of the GlaS challenge contest at MICCAI'2015 demonstrate that our method achieves competitive performance.

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“…Nevertheless, the initial contour still has to be manually labeled during the process of curve evolution. The approach [52] associated the level set method (LSE) model [32] with region and edge synergetic level set framework (RESLS) model [53] to improve segmentation results, which is able to efficiently segment images with unevenly distributed intensity and extends the two-phase model to multi-phase model. However, this model is sensitive to the choice of parameters and incompetent to in effectively processing natural images with complicated background information.…”

Section: Introductionmentioning

confidence: 99%

An overview of intelligent image segmentation using active contour models

Chen¹,

Ge²,

Wang³

et al. 2023

Intell Robot

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The active contour model (ACM) approach in image segmentation is regarded as a research hotspot in the area of computer vision, which is widely applied in different kinds of applications in practice, such as medical image processing. The essence of ACM is to make use ofuse an enclosed and smooth curve to signify the target boundary, which is usually accomplished by minimizing the associated energy function by means ofthrough the standard descent method. This paper presents an overview of ACMs for handling image segmentation problems in various fields. It begins with an introduction briefly reviewing different ACMs with their pros and cons. Then, some basic knowledge in of the theory of ACMs is explained, and several popular ACMs in terms of three categories, including region-based ACMs, edge-based ACMs, and hybrid ACMs, are detailedly reviewed with their advantages and disadvantages. After that, twelve ACMs are chosen from the literature to conduct three sets of segmentation experiments to segment different kinds of images, and compare the segmentation efficiency and accuracy with different methods. Next, two deep learning-based algorithms are implemented to segment different types of images to compare segmentation results with several ACMs. Experimental results confirm some useful conclusions about their sharing strengths and weaknesses. Lastly, this paper points out some promising research directions that need to be further studied in the future.

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Efficient active contour model for medical image segmentation and correction based on edge and region information

Cited by 33 publications

References 41 publications

Automatic 3D coronary artery segmentation based on local region active contour model

Automatic 3D coronary artery segmentation based on local region active contour model

UJAT-Net: A U-Net Combined Joint-Attention and Transformer for Breast Tubule Segmentation in H&E Stained Images

An overview of intelligent image segmentation using active contour models

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