Deep Learning for Breast Region and Pectoral Muscle Segmentation in Digital Mammography

Wang, Kaier; Khan, Nabeel; Chan, Ariane; Dunne, Jonathan; Highnam, Ralph

doi:10.1007/978-3-030-34879-3_7

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…182 The performance of the proposed models finally gave a mean percent overlap area (POA) of 93.7% ± 6.9% while the POA of TFO was only 86.9% ± 16.0%. Other similar works can be found in Wang et al 183 and Liu et al 184 More pectoral muscle segmentation works can be found in Moghbel et al 185 The subjective assessment of breast density can serve as a reliable predictor of breast cancer than other automated or semiautomated methods. 186 Dense breast tissues make breast cancer more difficult being identified and could increase the risk of breast cancer.…”

Section: Other Segmentation Scenarios In Breast Cancermentioning

confidence: 91%

A systematic survey of deep learning in breast cancer

Zhou

Wang

et al. 2021

Int J Intell Syst

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In recent years, we witnessed a speeding development of deep learning in computer vision fields like categorization, detection, and semantic segmentation. Within several years after the emergence of AlexNet, the performance of deep neural networks has already surpassed human being experts in certain areas and showed great potential in applications such as medical image analysis. The development of automated breast cancer detection systems that integrate deep learning has received wide attention from the community. Breast cancer, a major killer of females that results in millions of deaths, can be controlled even be cured given that it is detected at an early stage with sophisticated systems. In this paper, we reviewed breast cancer diagnosis, detection, and segmentation computer-aided (CAD) systems based on state-of-the-art deep convolutional neural networks. The available data sets also indirectly determine CAD systems' performance, so we introduced and discussed the details of public data sets. The challenges remaining in CAD systems for breast cancer are discussed at the end of this paper. The highlights of this survey mainly come from three following aspects. First, we covered a wide range of the basics of breast cancer from imaging modalities to popular databases in the community; Second, we presented the key elements in deep learning to form the compactness for methods mentioned in reviewed papers; Third and lastly, the summative details in each reviewed paper are provided so that interested readers can have a refined version of these works without referring to original papers.Therefore, this systematic survey suits readers with varied backgrounds and will be beneficial to them.

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Section: Other Segmentation Scenarios In Breast Cancermentioning

confidence: 91%

A systematic survey of deep learning in breast cancer

Zhou

Wang

et al. 2021

Int J Intell Syst

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“…Wang et al [20] first applied some image normalization to the input image and then trained the model using 2000 digital images with a dice-similarity coefficient of 0.8879 based on 825 of those images. Ali et al [21] are also using this technique with Gaussian and median filters.…”

Section: Deep Learning-based Algorithmsmentioning

confidence: 99%

Developing an Improved Method to Remove Pectoral Muscle for Better Diagnosis of Breast Cancer in Mammography Images

Abaei,

Rezaei,

Mian

et al. 2023

IJACSA

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Mammography is a non-invasive method to study breast tissues for abnormalities. Computer-aided diagnosis (CAD) can automate the process of diagnosing malignant and benign tumors accurately. However, accurate results can be hampered by the presence of the pectoral muscle, which has a similar opacity to the breast tissue area. Detecting and removing pectoral muscles is not trivial due to various factors, and there are artifacts present near the pectoral muscle that can hamper proper segmentation. Given the significance of the topic, it is crucial to devise an accurate method for automatically detecting the muscle area in a mammography image and eliminating it from the rest of the image. This process of removing the pectoral muscle from the breast image can aid in precise segmentation and diagnosis of the tumor area, ultimately leading to faster diagnosis and better outcomes for patients. This study examined two segmentation algorithms, Level Set and Region Growing, for segmenting the pectoral muscle. An Improved Region Growingbased (IRG) algorithm was also proposed and showed promising results in automatically segmenting the pectoral muscle. All algorithms were tested on the MIAS dataset, and radiologists evaluated the results, showing an accuracy rating of up to 83% for IRG. The results indicated that IRG outperformed Level Set considerably due to many optimizations and modifications. IRG can be used as part of the preprocessing unit of an automated cancer diagnosis system.

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“…Benefitting from facilitating advancement of deep learning, the segmentation task has experienced significant changes as well. For breast pectoral segmentation, there are also some deep learning-based methods [ 19 , 20 , 21 , 22 ]. In [ 20 ], U-Net was trained on a merged dataset that had 633 MLO view mammograms in the first stage.…”

Section: Related Workmentioning

confidence: 99%

PeMNet for Pectoral Muscle Segmentation

Wang

Górriz

et al. 2022

Biology

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As an important imaging modality, mammography is considered to be the global gold standard for early detection of breast cancer. Computer-Aided (CAD) systems have played a crucial role in facilitating quicker diagnostic procedures, which otherwise could take weeks if only radiologists were involved. In some of these CAD systems, breast pectoral segmentation is required for breast region partition from breast pectoral muscle for specific analysis tasks. Therefore, accurate and efficient breast pectoral muscle segmentation frameworks are in high demand. Here, we proposed a novel deep learning framework, which we code-named PeMNet, for breast pectoral muscle segmentation in mammography images. In the proposed PeMNet, we integrated a novel attention module called the Global Channel Attention Module (GCAM), which can effectively improve the segmentation performance of Deeplabv3+ using minimal parameter overheads. In GCAM, channel attention maps (CAMs) are first extracted by concatenating feature maps after paralleled global average pooling and global maximum pooling operation. CAMs are then refined and scaled up by multi-layer perceptron (MLP) for elementwise multiplication with CAMs in next feature level. By iteratively repeating this procedure, the global CAMs (GCAMs) are then formed and multiplied elementwise with final feature maps to lead to final segmentation. By doing so, CAMs in early stages of a deep convolution network can be effectively passed on to later stages of the network and therefore leads to better information usage. The experiments on a merged dataset derived from two datasets, INbreast and OPTIMAM, showed that PeMNet greatly outperformed state-of-the-art methods by achieving an IoU of 97.46%, global pixel accuracy of 99.48%, Dice similarity coefficient of 96.30%, and Jaccard of 93.33%, respectively.

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Deep Learning for Breast Region and Pectoral Muscle Segmentation in Digital Mammography

Cited by 9 publications

References 25 publications

A systematic survey of deep learning in breast cancer

A systematic survey of deep learning in breast cancer

Developing an Improved Method to Remove Pectoral Muscle for Better Diagnosis of Breast Cancer in Mammography Images

PeMNet for Pectoral Muscle Segmentation

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