scholarly journals PeMNet for Pectoral Muscle Segmentation

Biology ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 134
Author(s):  
Xiang Yu ◽  
Shui-Hua Wang ◽  
Juan Manuel Górriz ◽  
Xian-Wei Jiang ◽  
David S. Guttery ◽  
...  

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.

2019 ◽  
Vol 9 (4) ◽  
pp. 481-496 ◽  
Author(s):  
Santhos Kumar Avuti ◽  
Varun Bajaj ◽  
Anil Kumar ◽  
Girish Kumar Singh

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaodong Huang ◽  
Hui Zhang ◽  
Li Zhuo ◽  
Xiaoguang Li ◽  
Jing Zhang

Extracting the tongue body accurately from a digital tongue image is a challenge for automated tongue diagnoses, as the blurred edge of the tongue body, interference of pathological details, and the huge difference in the size and shape of the tongue. In this study, an automated tongue image segmentation method using enhanced fully convolutional network with encoder-decoder structure was presented. In the frame of the proposed network, the deep residual network was adopted as an encoder to obtain dense feature maps, and a Receptive Field Block was assembled behind the encoder. Receptive Field Block can capture adequate global contextual prior because of its structure of the multibranch convolution layers with varying kernels. Moreover, the Feature Pyramid Network was used as a decoder to fuse multiscale feature maps for gathering sufficient positional information to recover the clear contour of the tongue body. The quantitative evaluation of the segmentation results of 300 tongue images from the SIPL-tongue dataset showed that the average Hausdorff Distance, average Symmetric Mean Absolute Surface Distance, average Dice Similarity Coefficient, average precision, average sensitivity, and average specificity were 11.2963, 3.4737, 97.26%, 95.66%, 98.97%, and 98.68%, respectively. The proposed method achieved the best performance compared with the other four deep-learning-based segmentation methods (including SegNet, FCN, PSPNet, and DeepLab v3+). There were also similar results on the HIT-tongue dataset. The experimental results demonstrated that the proposed method can achieve accurate tongue image segmentation and meet the practical requirements of automated tongue diagnoses.


2013 ◽  
Vol 110 (1) ◽  
pp. 48-57 ◽  
Author(s):  
Karthikeyan Ganesan ◽  
U. Rajendra Acharya ◽  
Kuang Chua Chua ◽  
Lim Choo Min ◽  
K. Thomas Abraham

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Woong Bae Yoon ◽  
Ji Eun Oh ◽  
Eun Young Chae ◽  
Hak Hee Kim ◽  
Soo Yeul Lee ◽  
...  

The computer-aided detection (CAD) systems have been developed to help radiologists with the early detection of breast cancer. This system provides objective and accurate information to reduce the misdiagnosis of the disease. In mammography, the pectoral muscle region is used as an index to compare the symmetry between the left and right images in the mediolateral oblique (MLO) view. The pectoral muscle segmentation is necessary for the detection of microcalcification or mass because the pectoral muscle has a similar pixel intensity as that of lesions, which affects the results of automatic detection. In this study, the mammographic image analysis society database (MIAS, 322 cases) was used for detecting the pectoral muscle segmentation. The pectoral muscle was detected by using the morphological method and the random sample consensus (RANSAC) algorithm. We evaluated the detected pectoral muscle region and compared the manual segmentation with the automatic segmentation. The results showed 92.2% accuracy. We expect that the proposed method improves the detection accuracy of breast cancer lesions using a CAD system.


2019 ◽  
Vol 57 ◽  
pp. 1-17 ◽  
Author(s):  
Andrik Rampun ◽  
Karen López-Linares ◽  
Philip J. Morrow ◽  
Bryan W. Scotney ◽  
Hui Wang ◽  
...  

2022 ◽  
Vol 15 (1) ◽  
pp. 1-14
Author(s):  
Divyashree B. V. ◽  
Amarnath R. ◽  
Naveen M. ◽  
Hemantha Kumar G.

In this paper, pectoral muscle segmentation was performed to study the presence of malignancy in the pectoral muscle region in mammograms. A combined approach involving granular computing and layering was employed to locate the pectoral muscle in mammograms. In most cases, the pectoral muscle is found to be triangular in shape and hence, the ant colony optimization algorithm is employed to accurately estimate the pectoral muscle boundary. The proposed method works with the left mediolateral oblique (MLO) view of mammograms to avoid artifacts. For the right MLO view, the method automatically mirrors the image to the left MLO view. The performance of this method was evaluated using the standard mini MIAS dataset (mammographic image analysis society). The algorithm was tested on 322 images and the overall accuracy of the system was about 97.47 %. The method is robust with respect to the view, shape, size and reduces the processing time. The approach correctly identifies images when the pectoral muscle is completely absent.


2014 ◽  
Vol 39 (8) ◽  
pp. 1267-1272
Author(s):  
Yan-Feng LI ◽  
Hou-Jin CHEN ◽  
Na YANG ◽  
Sheng-Jun ZHANG

2013 ◽  
Vol 46 (3) ◽  
pp. 681-691 ◽  
Author(s):  
Yanfeng Li ◽  
Houjin Chen ◽  
Yongyi Yang ◽  
Na Yang

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