scholarly journals CMM-Net: Contextual Multi-Scale Multi-Level Network for Efficient Biomedical Image Segmentation

2021 ◽  
Author(s):  
Mohammed Al-masni ◽  
Dong-Hyun Kim
Keyword(s):  
Image Segmentation ◽  
Blood Vessels ◽  
Medical Image ◽  
Spatial Scales ◽  
Medical Image Analysis ◽  
Skin Lesions ◽  
Medical Image Segmentation ◽  
Multi Scale ◽  
Retinal Blood Vessels ◽  
Multi Level

Abstract Medical image segmentation of tissue abnormalities, key organs, or blood vascular system is of great significance for any computerized diagnostic system. However, automatic segmentation in medical image analysis is a challenging task since it requires sophisticated knowledge of the target organ anatomy. This paper develops an end-to-end deep learning segmentation method called Contextual Multi-Scale Multi-Level Network (CMM-Net). The main idea is to fuse the global contextual features of multiple spatial scales at every contracting convolutional network level in the U-Net. Also, we re-exploit the dilated convolution module that enables an expansion of the receptive field with different rates depending on the size of feature maps throughout the networks. In addition, an augmented testing scheme referred to as Inversion Recovery (IR) which uses logical “OR” and “AND” operators is developed. The proposed segmentation network is evaluated on three medical imaging datasets, namely ISIC 2017 for skin lesions segmentation from dermoscopy images, DRIVE for retinal blood vessels segmentation from fundus images, and BraTS 2018 for brain gliomas segmentation from MR scans. The experimental results showed superior state-of-the-art performance with overall dice similarity coefficients of 85.78%, 80.27%, and 88.96% on the segmentation of skin lesions, retinal blood vessels, and brain tumors, respectively. The proposed CMM-Net is inherently general and could be efficiently applied as a robust tool for various medical image segmentations.

scholarly journals CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammed A. Al-masni ◽  
Dong-Hyun Kim
Keyword(s):  
Image Segmentation ◽  
Blood Vessels ◽  
Medical Image ◽  
Spatial Scales ◽  
Medical Image Analysis ◽  
Skin Lesions ◽  
Medical Image Segmentation ◽  
Multi Scale ◽  
Retinal Blood Vessels ◽  
Multi Level

AbstractMedical image segmentation of tissue abnormalities, key organs, or blood vascular system is of great significance for any computerized diagnostic system. However, automatic segmentation in medical image analysis is a challenging task since it requires sophisticated knowledge of the target organ anatomy. This paper develops an end-to-end deep learning segmentation method called Contextual Multi-Scale Multi-Level Network (CMM-Net). The main idea is to fuse the global contextual features of multiple spatial scales at every contracting convolutional network level in the U-Net. Also, we re-exploit the dilated convolution module that enables an expansion of the receptive field with different rates depending on the size of feature maps throughout the networks. In addition, an augmented testing scheme referred to as Inversion Recovery (IR) which uses logical “OR” and “AND” operators is developed. The proposed segmentation network is evaluated on three medical imaging datasets, namely ISIC 2017 for skin lesions segmentation from dermoscopy images, DRIVE for retinal blood vessels segmentation from fundus images, and BraTS 2018 for brain gliomas segmentation from MR scans. The experimental results showed superior state-of-the-art performance with overall dice similarity coefficients of 85.78%, 80.27%, and 88.96% on the segmentation of skin lesions, retinal blood vessels, and brain tumors, respectively. The proposed CMM-Net is inherently general and could be efficiently applied as a robust tool for various medical image segmentations.

scholarly journals Match Feature U-Net: Dynamic Receptive Field Networks for Biomedical Image Segmentation

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1230
Author(s):  
Xiaofei Qin ◽  
Chengzi Wu ◽  
Hang Chang ◽  
Hao Lu ◽  
Xuedian Zhang
Keyword(s):  
Image Segmentation ◽  
Receptive Field ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Training Data ◽  
Cell Segmentation ◽  
Biomedical Image ◽  
Nuclei Segmentation ◽  
Kernel Convolution

Medical image segmentation is a fundamental task in medical image analysis. Dynamic receptive field is very helpful for accurate medical image segmentation, which needs to be further studied and utilized. In this paper, we propose Match Feature U-Net, a novel, symmetric encoder– decoder architecture with dynamic receptive field for medical image segmentation. We modify the Selective Kernel convolution (a module proposed in Selective Kernel Networks) by inserting a newly proposed Match operation, which makes similar features in different convolution branches have corresponding positions, and then we replace the U-Net’s convolution with the redesigned Selective Kernel convolution. This network is a combination of U-Net and improved Selective Kernel convolution. It inherits the advantages of simple structure and low parameter complexity of U-Net, and enhances the efficiency of dynamic receptive field in Selective Kernel convolution, making it an ideal model for medical image segmentation tasks which often have small training data and large changes in targets size. Compared with state-of-the-art segmentation methods, the number of parameters in Match Feature U-Net (2.65 M) is 34% of U-Net (7.76 M), 29% of UNet++ (9.04 M), and 9.1% of CE-Net (29 M). We evaluated the proposed architecture in four medical image segmentation tasks: nuclei segmentation in microscopy images, breast cancer cell segmentation, gland segmentation in colon histology images, and disc/cup segmentation. Our experimental results show that Match Feature U-Net achieves an average Mean Intersection over Union (MIoU) gain of 1.8, 1.45, and 2.82 points over U-Net, UNet++, and CE-Net, respectively.

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