MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures

Adoui, Mohammed El; Mahmoudi, Sidi Ahmed; Larhmam, Mohamed Amine; Benjelloun, M.

doi:10.3390/computers8030052

Cited by 84 publications

(71 citation statements)

References 35 publications

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“…For example, a fully convolutional network (FCN) can classify images at the pixel level [19][20][21][22][23][24], and its multi-scale feature fusion structure improves the accuracy of image segmentation. Compared with the FCN structure, a SegNet structure uses the pooled index calculated in the maximum pooling step of the corresponding encoder in the decoder structure to perform a nonlinear upsampling step, which can save more memory space and there is no need to update parameters in the upsampling stage, and many improved deep-learning models are based on the SegNet structure [25][26][27][28][29][30]. In order to improve the use of feature information in images, the long connection structure of UNet is also widely used in image segmentation, and this structure achieves the fusion of multi-scale image information to improve segmentation performance.…”

Section: Introductionmentioning

confidence: 99%

Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

Lin

Wang

et al. 2020

Remote Sensing

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Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.

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Section: Introductionmentioning

confidence: 99%

Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

Lin

Wang

et al. 2020

Remote Sensing

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“…Xu et al used a 2D U-Net for automatic breast region segmentation in DCE-MRI [12]. Adoui et al built two fully CNNs based on SegNet and U-Net for breast tumor segmentation [13]. Building on the success of U-Net in medical image segmentation, this study additionally used U-Net++ [14] for comparison of DCE-MRI images for breast region segmentation.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI

Han

Jiang

Pang

et al. 2020

Computational and Mathematical Methods in Medicine

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Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++. Using the method of deep learning to extract the target area can help to reduce the interference external to the breast. Second, a faster region with convolutional neural network (Faster RCNN) was used for mass detection on segmented breast images. The dataset of DCE-MRI used in this study was obtained from 75 patients, and a 5-fold cross validation method was adopted. The statistical analysis of breast region segmentation was carried out by computing the Dice similarity coefficient (DSC), Jaccard coefficient, and segmentation sensitivity. For validation of breast mass detection, the sensitivity with the number of false positives per case was computed and analyzed. The Dice and Jaccard coefficients and the segmentation sensitivity value for breast region segmentation were 0.951, 0.908, and 0.948, respectively, which were better than those of the original U-Net algorithm, and the average sensitivity for mass detection achieved 0.874 with 3.4 false positives per case.

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“…GCN (2017) [30] used a large convolution kernel and decomposed the convolution kernel of a large kxk into two, 1xk and kx1, to balance the accuracy contradiction between location and classification. [33] proposed a different encoder and decoder CNN architectures to automate the breast tumor segmentation in dynamic-contrast-enhanced magnetic resonance imaging based on SegNet [25] and U-Net [24].…”

Section: Semantic Segmentation Based On a Deep Convolutional Neural Nmentioning

confidence: 99%

ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features

et al. 2020

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River ice monitoring is of great significance for river management, ship navigation and ice hazard forecasting in cold-regions. Accurate ice segmentation is one most important pieces of technology in ice monitoring research. It can provide the prerequisite information for the calculation of ice cover density, drift ice speed, ice cover distribution, change detection and so on. Unmanned aerial vehicle (UAV) aerial photography has the advantages of higher spatial and temporal resolution. As UAV technology has become more popular and cheaper, it has been widely used in ice monitoring. So, we focused on river ice segmentation based on UAV remote sensing images. In this study, the NWPU_YRCC dataset was built for river ice segmentation, in which all images were captured by different UAVs in the region of the Yellow River, the most difficult river to manage in the world. To the best of our knowledge, this is the first public UAV image dataset for river ice segmentation. Meanwhile, a semantic segmentation deep convolution neural network by fusing positional and channel-wise attentive features is proposed for river ice semantic segmentation, named ICENET. Experiments demonstrated that the proposed ICENET outperforms the state-of-the-art methods, achieving a superior result on the NWPU_YRCC dataset.

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MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures

Cited by 84 publications

References 35 publications

Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI

ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features

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