Automatic breast tissue segmentation in MRIs with morphology snake and deep denoiser training via extended Stein’s unbiased risk estimator

Yin, Xiaoxia; Jian, Yunxiang; Zhang, Yang; Zhang, Yanchun; Wu, Jianlin; Lu, Hui; Su, Min‐Ying

doi:10.1007/s13755-021-00143-x

Cited by 5 publications

(9 citation statements)

References 47 publications

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“…At point p, the boundary loss function is defined as follows: (9) Similarly, segmentation loss function is shown as Eq. ( 5), which combines the weighted IoU loss with the standard cross-entropy (BCE) loss, and the final loss is extended to: (10)…”

Section: Bce-based Boundary Lossmentioning

confidence: 99%

“…The process to separate objects from their respective backgrounds is often known as interactive object selection or interactive segmentation which is commonly required in many image editing and visual analysis workflows [7][8][9]. While recent advanced methods of interactive segmentation focus on the region-based paradigm, more traditional boundarybased methods, such as the binary level set, are still popular in practice as they allow users to have active control over the object boundaries [10][11][12][13]. The main limitation faced by existing boundary-based segmentation methods, however, is that much more user input is often demanded.…”

Section: Introductionmentioning

confidence: 99%

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Focal Boundary Dice: Improved Breast Tumor Segmentation from MRI Scan

Yin¹,

Jian²,

Shen³

et al. 2023

J. Cancer

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Focal Boundary Dice, a new segmentation evaluation measure, was hereby presented, with the focus on boundary quality and class imbalance. Extensive analysis was carried out across different error types with varied object sizes of imaged tumors from Magnetic Resonance Imaging (MRI) scans, and the results show that Focal Boundary Dice is significantly more adaptive than the standard Focal and Dice measures to boundary errors for imaged tumors from MRI scans and does not over-penalize errors on the division of the boundary, including smaller imaged objects. Based on Boundary Dice, the standard evaluation protocols for tumor segmentation tasks were updated by proposing the Focal Boundary Dice. The contradiction between the target and the background area, and the conflict between the importance and the attention of boundary features were mainly solved. Meanwhile, a boundary attention module was introduced to further extract the tumor edge features. The new quality measure presents several desirable characteristics, including higher accuracy in the selection of hard samples, prediction/ground-truth pairs, and balanced responsiveness with across scales, which jointly make it more suitable for segmentation evaluation than other classification-focused measures such as combined Intersection-over-Union and Boundary binary cross-entropy loss, Boundary binary cross-entropy loss and Shape-aware Loss. The experiments show that the new evaluation metrics allow boundary quality improvements and image segmentation accuracy that are generally overlooked by current Dice-based evaluation metrics and deep learning models. It is expected that the adoption of the new boundary-adaptive evaluation metrics will facilitate the rapid progress in segmentation methods, and further contribute to the improvement of classification accuracy.

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Section: Bce-based Boundary Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Focal Boundary Dice: Improved Breast Tumor Segmentation from MRI Scan

Yin¹,

Jian²,

Shen³

et al. 2023

J. Cancer

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“…Then, the user selects a rough area, and the algorithm automatically generates a 3D image of the lesion, which is then manually refined on the 3D surface. Yin et al (32) proposed a novel, fast, and fully automated morphology segmentation algorithm for dividing breast tissue in breast MR images with accuracy and precision that exceeds those of the existing methods. Huang et al (33) compared and analyzed thresholding-, clustering-, and watershed-based segmentation architectures in breast US images recently and concluded that each technique has benefits and drawbacks.…”

Section: Image Segmentationmentioning

confidence: 99%

“…Yin et al. ( 32 ) proposed a novel, fast, and fully automated morphology segmentation algorithm for dividing breast tissue in breast MR images with accuracy and precision that exceeds those of the existing methods. Huang et al.…”

Section: Radiomicsmentioning

confidence: 99%

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

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The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

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“…Morphological geodesic active contour gradually develops segmentation areas and then morphologically segments the objects. This method combines the morphology snake algorithm and the geodesic active contour algorithm, which performs object detection using morphology operators [8][9][10][11][12][13]. Morphological geodesic active content has the advantage of being able to morphologically segment objects that are not in clear forms, such as squares and triangles.…”

Section: Introductionmentioning

confidence: 99%

Classification of a 3D Film Pattern Image Using the Optimal Height of the Histogram for Quality Inspection

et al. 2023

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A 3D film pattern image was recently developed for marketing purposes, and an inspection method is needed to evaluate the quality of the pattern for mass production. However, due to its recent development, there are limited methods to inspect the 3D film pattern. The good pattern in the 3D film has a clear outline and high contrast, while the bad pattern has a blurry outline and low contrast. Due to these characteristics, it is challenging to examine the quality of the 3D film pattern. In this paper, we propose a simple algorithm that classifies the 3D film pattern as either good or bad by using the height of the histograms. Despite its simplicity, the proposed method can accurately and quickly inspect the 3D film pattern. In the experimental results, the proposed method achieved 99.09% classification accuracy with a computation time of 6.64 s, demonstrating better performance than existing algorithms.

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Automatic breast tissue segmentation in MRIs with morphology snake and deep denoiser training via extended Stein’s unbiased risk estimator

Cited by 5 publications

References 47 publications

Focal Boundary Dice: Improved Breast Tumor Segmentation from MRI Scan

Focal Boundary Dice: Improved Breast Tumor Segmentation from MRI Scan

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Classification of a 3D Film Pattern Image Using the Optimal Height of the Histogram for Quality Inspection

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