Adaptive weighted curvature-based active contour for ultrasonic and 3T/5T MR image segmentation

Pang, Zhi-Feng; Geng, Meng-xiao; Zhang, Lan; Zhou, Yanru; Zeng, Tieyong; Zhang, Na; Dong, Liang; Zheng, Hairong; Dai, Yongming; Huang, Zhenxing; Hu, Zhanli

doi:10.1016/j.sigpro.2022.108881

Cited by 16 publications

(8 citation statements)

References 39 publications

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“…Moreover, we plan to determine how to make the sparsity parameter α in AITV adaptable to each image. In future work, we will adapt other segmentation algorithms (Li et al, 2010(Li et al, , 2016Jung et al, 2014;Jung, 2017;Cai et al, 2019;Yang et al, 2022;Pang et al, 2023) designed for Gaussian noise or impulsive noise to Poisson noise.…”

Section: Discussionmentioning

confidence: 99%

Difference of anisotropic and isotropic TV for segmentation under blur and Poisson noise

Bui

Lou

Park

et al. 2023

Front. Comput. Sci.

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In this paper, we aim to segment an image degraded by blur and Poisson noise. We adopt a smoothing-and-thresholding (SaT) segmentation framework that finds a piecewise-smooth solution, followed by k-means clustering to segment the image. Specifically for the image smoothing step, we replace the least-squares fidelity for Gaussian noise in the Mumford-Shah model with a maximum posterior (MAP) term to deal with Poisson noise and we incorporate the weighted difference of anisotropic and isotropic total variation (AITV) as a regularization to promote the sparsity of image gradients. For such a nonconvex model, we develop a specific splitting scheme and utilize a proximal operator to apply the alternating direction method of multipliers (ADMM). Convergence analysis is provided to validate the efficacy of the ADMM scheme. Numerical experiments on various segmentation scenarios (grayscale/color and multiphase) showcase that our proposed method outperforms a number of segmentation methods, including the original SaT.

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Section: Discussionmentioning

confidence: 99%

Difference of anisotropic and isotropic TV for segmentation under blur and Poisson noise

Bui

Lou

Park

et al. 2023

Front. Comput. Sci.

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“…Traditional methods for cell nucleus segmentation can generally be categorized into the following strategies: threshold-based methods, morphological-based methods, and active contour models (Khamael et al 2020, Pang et al 2023. Among them, threshold-based methods offer the advantages of intuitiveness and straightforward implementation.…”

Section: Segmentation Methodsmentioning

confidence: 99%

An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images

Yang,

Lei,

Huang

et al. 2024

Phys. Med. Biol.

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Objective.Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, the high-density distribution of nuclei, and cell adhesion. Approach.In this paper, we present an interactive nuclei segmentation framework that increases the precision of nuclei segmentation. Our framework incorporates expert monitoring to gather as much prior information as possible and accurately segment complex nucleus images through limited pathologist interaction, where only a small portion of the nucleus locations in each image are labeled. The initial contour is determined by the Voronoi diagram generated from the labeled points, which is then input into an optimized weighted convex difference model to regularize partition boundaries in an image. Specifically, we provide theoretical proof of the mathematical model, stating that the objective function monotonically decreases. Furthermore, we explore a postprocessing stage that incorporates histograms, which are simple and easy to handle and prevent arbitrariness and subjectivity in individual choices. Main results. To evaluate our approach, we conduct experiments on both a cervical cancer dataset and a nasopharyngeal cancer dataset. The experimental results demonstrate that our approach achieves competitive performance compared to other methods. Significance. The Voronoi diagram in the paper serves as prior information for the active contour, providing positional information for individual cells. Moreover, the active contour model achieves precise segmentation results while offering mathematical interpretability.

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“…However, many hyperparameters need to be adjusted during experiments, which greatly affects the effectiveness of the model. To this end, Pang et al 2 introduced heat kernel convolution into the WBHV model, which not only reduces the computational complexity but also improves the segmentation accuracy. Traditional image segmentation methods have advantages in incorporating prior knowledge and imposing constraints on the shape of the segmented object.…”

Section: Segmentation Methodsmentioning

confidence: 99%

“…With the development of medical imaging equipment, X-rays, computed tomography (CT), 1 magnetic resonance imaging (MRI), 2 positron emission tomography (PET), 3 and ultrasonography have become important medical aids for patient health. Images acquired with these technologies benefit many applications, such as disease diagnosis, prognostic assessment, and surgical planning.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Few‐shot segmentation framework for lung nodules via an optimized active contour model

Yang,

Shao,

Huang

et al. 2024

Medical Physics

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BackgroundAccurate segmentation of lung nodules is crucial for the early diagnosis and treatment of lung cancer in clinical practice. However, the similarity between lung nodules and surrounding tissues has made their segmentation a longstanding challenge.PurposeExisting deep learning and active contour models each have their limitations. This paper aims to integrate the strengths of both approaches while mitigating their respective shortcomings.MethodsIn this paper, we propose a few‐shot segmentation framework that combines a deep neural network with an active contour model. We introduce heat kernel convolutions and high‐order total variation into the active contour model and solve the challenging nonsmooth optimization problem using the alternating direction method of multipliers. Additionally, we use the presegmentation results obtained from training a deep neural network on a small sample set as the initial contours for our optimized active contour model, addressing the difficulty of manually setting the initial contours.ResultsWe compared our proposed method with state‐of‐the‐art methods for segmentation effectiveness using clinical computed tomography (CT) images acquired from two different hospitals and the publicly available LIDC dataset. The results demonstrate that our proposed method achieved outstanding segmentation performance according to both visual and quantitative indicators.ConclusionOur approach utilizes the output of few‐shot network training as prior information, avoiding the need to select the initial contour in the active contour model. Additionally, it provides mathematical interpretability to the deep learning, reducing its dependency on the quantity of training samples.

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Adaptive weighted curvature-based active contour for ultrasonic and 3T/5T MR image segmentation

Cited by 16 publications

References 39 publications

Difference of anisotropic and isotropic TV for segmentation under blur and Poisson noise

Difference of anisotropic and isotropic TV for segmentation under blur and Poisson noise

An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images

Few‐shot segmentation framework for lung nodules via an optimized active contour model

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