Mammographic mass segmentation using multichannel and multiscale fully convolutional networks

Xu, Shengzhou; Adeli, Ehsan; Cheng, Jie; Xiang, Lei; Li, Yang; Lee, Seong Whan; Shen, Dinggang

doi:10.1002/ima.22423

Cited by 14 publications

(6 citation statements)

References 34 publications

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“…The proposed model also showed notable results to separate each BI-RADS breast density class where LIBRA failed. A multichannel and multiscale fully convolutional network for mammogram mass segmentation was proposed by Xu [117]. Preprocessing was carried out to reduce the influence of nearby structures that are negligible.…”

Section: Fcn For Mammogram Segmentationmentioning

confidence: 99%

“…Conversely, Unet in calcification detection achieved only 70.3% sensitivity on DDSM. Among FCN-based methods, the technique of Xu et al [117] proved to have remarkably high inference knowledge capabilities scoring 0.91 dice similarity coefficient on two different datasets (DDSM, INBreast) in mass segmentation. Bhatti et al [127] showed reasonably accurate performances of the Mask RCNN-FPN method on DDSM and INBreast in detection and segmentation of breast lesions (91% accuracy).…”

Section: Pros and Cons Of Deep Learning Approachesmentioning

confidence: 99%

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A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

et al. 2021

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Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

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Section: Fcn For Mammogram Segmentationmentioning

confidence: 99%

Section: Pros and Cons Of Deep Learning Approachesmentioning

confidence: 99%

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

et al. 2021

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“…Among the abnormality types, masses are reportedly major contributors to breast cancer [16]. Therefore, in this paper, we aim to address the identification of masses that generally has two forms: mass detection [17][18][19][20] and mass segmentation [21][22][23][24][25]. Mass segmentation provides more comprehensive information, including border information; hence, in this paper, the main task is mass segmentation.…”

Section: Introductionmentioning

confidence: 99%

Improved Loss Function for Mass Segmentation in Mammography Images Using Density and Mass Size

Aliniya,

Nicolescu,

Nicolescu

et al. 2024

J. Imaging

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Mass segmentation is one of the fundamental tasks used when identifying breast cancer due to the comprehensive information it provides, including the location, size, and border of the masses. Despite significant improvement in the performance of the task, certain properties of the data, such as pixel class imbalance and the diverse appearance and sizes of masses, remain challenging. Recently, there has been a surge in articles proposing to address pixel class imbalance through the formulation of the loss function. While demonstrating an enhancement in performance, they mostly fail to address the problem comprehensively. In this paper, we propose a new perspective on the calculation of the loss that enables the binary segmentation loss to incorporate the sample-level information and region-level losses in a hybrid loss setting. We propose two variations of the loss to include mass size and density in the loss calculation. Also, we introduce a single loss variant using the idea of utilizing mass size and density to enhance focal loss. We tested the proposed method on benchmark datasets: CBIS-DDSM and INbreast. Our approach outperformed the baseline and state-of-the-art methods on both datasets.

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“…The preprocesses of feature selection and feature extraction are used to reduce the data dimensionality for overcoming this drawback. The deep CNN-based methods combine the multiconvolutional pooling layers (>10 layers in general configuration) and a classification layer to perform the automatic end-to-end enhancement process, noise filtering, feature extraction, and pattern recognition in this proposed topic, such as amass classification, lesion detection and localization, and lesion segmentation/ROI detection, by using fully convolutional network (FCN), Unet CNN, region-based CNN (R-CNN), faster R-CNN, TTCNN (transferable texture convolutional neural network), and Grad-CAM (gradient-weighted class activation mapping)-based CNN [31][32][33][34][35][36][37][38]. The multiconvolutional-pooling processes can extract the desired features from low-level features to high-level information (sharpening process) for detecting nor-mality objects, and then can increase the detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Breast Lesions Screening of Mammographic Images with 2D Spatial and 1D Convolutional Neural Network-Based Classifier

et al. 2022

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Mammography is a first-line imaging examination that employs low-dose X-rays to rapidly screen breast tumors, cysts, and calcifications. This study proposes a two-dimensional (2D) spatial and one-dimensional (1D) convolutional neural network (CNN) to early detect possible breast lesions (tumors) to reduce patients’ mortality rates and to develop a classifier for use in mammographic images on regions of interest where breast lesions (tumors) may likely occur. The 2D spatial fractional-order convolutional processes are used to strengthen and sharpen the lesions’ features, denoise, and improve the feature extraction processes. Then, an automatic extraction task is performed using a specific bounding box to sequentially pick out feature patterns from each mammographic image. The multi-round 1D kernel convolutional processes can also strengthen and denoise 1D feature signals and assist in the identification of the differentiation levels of normality and abnormality signals. In the classification layer, a gray relational analysis-based classifier is used to screen the possible lesions, including normal (Nor), benign (B), and malignant (M) classes. The classifier development for clinical applications can reduce classifier’s training time, computational complexity level, computational time, and achieve a more accurate rate for meeting clinical/medical purpose. Mammographic images were selected from the mammographic image analysis society image database for experimental tests on breast lesions screening and K-fold cross-validations were performed. The experimental results showed promising performance in quantifying the classifier’s outcome for medical purpose evaluation in terms of recall (%), precision (%), accuracy (%), and F1 score.

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Mammographic mass segmentation using multichannel and multiscale fully convolutional networks

Cited by 14 publications

References 34 publications

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

Improved Loss Function for Mass Segmentation in Mammography Images Using Density and Mass Size

Breast Lesions Screening of Mammographic Images with 2D Spatial and 1D Convolutional Neural Network-Based Classifier

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