The fusing of dilated convolution and attention for segmentation of gastric cancer tissue sections

Abstract

Objective: As the gold standard for the diagnosis of gastric cancer, pathological section has been a hotspot nowadays. The degree of precise detection of the lesion area in the section has rather beneficial to in-situ diagnosis and follow-up treatment. The pathologists have missed some subtle changes in cancerous cells in their practice. Automated gastric cancer cells segmentation has been aided to diagnose. Deep learning-based pathological section image of stomach have been obtained qualified classification via deep convolutional neural networks (DCNNs). Focused segmentation for pathological section has been challenged to some issues as below. First, the color and morphology between gastric cancer cells and normal cells to extract deep features. Second, the different magnifications in pathological section images have been hard to segment in different sizes. A semantic segmentation neural network called attention-dilated-efficient U-Net++ (ADEU-Net) has been demonstrated rather than original U-Net to facilitate the precision of gastric cancer cell segmentation. Method: The illustrated framework is an encoder-decoder networks which can achieve end-to-end training. The capability of the encoder has affected the segmentation accuracy based on the deep features of pathological section images interpretation. The featured extraction part of EfficientNet has been adopted as the encoder via qualified classification. The initialized weights of EfficientNet have been pre-trained on ImageNet and its structure have been divided into five stages for the skipped cohesions. The decoder has been designed in terms of the structure of U-Net++. The encoder and decoder sub-networks have been via the integration of nested, dense skip pathways. An 8 GB GPU model training has been implemented based on the most skip cohesions in U-Net++. The convolution blocks in the decoder has been re-modified to the gradient transfer issues. An additional module called DBlock has been integrated to enhance the feature extraction capability for multi-sized pathological sections. Three multi-layers dilation rates convolution have been cascaded in DBlock to realize the features in receptive fields. The dilation rates of the stacked dilated convolution layers have been calculated to 1, 2, 5 and the receptive field of each layer has been realized 3, 7, 17 each in terms of the structure of hybrid dilated convolution (HDC). The featured maps have been concatenated by channel and fused via a 1×1 convolution layer to realize multi-scale features simultaneously. The attention mechanism has been used to replace the skip connection between the encoder and the decoder to suppress the feature correspondence of the background region effectively as well. The outputs of the encoder and the decoder of the upper layer have been conducted each based on a 1×1 convolution layer. The optional assigned weights to the parameters of the original feature map have been added together to form the attention gate. deep supervised learning can be altered to solve the low speed convergence in the training process. Result: The experiments on two datasets called SEED and BOT have been conducted to verify the effectiveness of the method obtained from two gastric cancer cell section segmentation competition. The evaluation metrics of the models have Dice coefficient, sensitivity, pixel-wise accuracy and precision. Different segmentation results have also been calculated visually. First the baseline method has been compared to some classical models on SEED dataset in 18.96% accuracy higher than original U-Net and it has been found that the design of feature extraction has been crucial to the segmentation accuracy. Transfer-learning strategies of the encoder have been improved the results greatly. The further ablation experiments have been performed to each added module to confirm the results of segmentation., The Dice coefficient, sensitivity, accuracy and precision has been increased by 5.17% and 0.47%, 2.7% and 0.06%, 3.69% and 4.30%, 4.08% and 6.08% each compared with the baseline model's results with SEED and BOT. The results have demonstrated the effectiveness of each part of proposed algorithm. The visual segmentation results have more similar to the ground truth label. Conclusion: A semantic segmentation model called ADEU-Net has been illustrated to the segmentation of pathological sections of gastric cancer task. The involvement of EfficientNet has beneficial to feature extraction, multi-scale features assembling cascade dilated convolution layers and the attention module in replace of the skip connection between the encoder and decoder.