PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation.

Accurate segmentation of the medical image is the basis and premise of intelligent diagnosis and treatment, which has a wide range of clinical application value. However, the robustness and effectiveness of medical image segmentation algorithms remains a challenging subject due to the unbalanced categories, blurred boundaries, highly variable anatomical structures and lack of training samples. For this reason, we present a parallel dilated convolutional network (PDC-Net) to address the pituitary adenoma segmentation in magnetic resonance imaging images. Firstly, the standard convolution block in U-Net is replaced by a basic convolution operation and a parallel dilated convolutional module (PDCM), to extract the multi-level feature information of different dilations. Furthermore, the channel attention mechanism (CAM) is integrated to enhance the ability of the network to distinguish between lesions and non-lesions in pituitary adenoma. Then, we introduce residual connections at each layer of the encoder-decoder, which can solve the problem of gradient disappearance and network performance degradation caused by network deepening. Finally, we employ the dice loss to deal with the class imbalance problem in samples. By testing on the self-established patient dataset from Quzhou People's Hospital, the experiment achieves 90.92% of Sensitivity, 99.68% of Specificity, 88.45% of Dice value and 79.43% of Intersection over Union (IoU).

Modified UNet++ with atrous spatial pyramid pooling for blood cell image segmentation.

Blood cell image segmentation is an important part of the field of computer-aided diagnosis. However, due to the low contrast, large differences in cell morphology and the scarcity of labeled images, the segmentation performance of cells cannot meet the requirements of an actual diagnosis. To address the above limitations, we present a deep learning-based approach to study cell segmentation on pathological images. Specifically, the algorithm selects UNet++ as the backbone network to extract multi-scale features. Then, the skip connection is redesigned to improve the degradation problem and reduce the computational complexity. In addition, the atrous spatial pyramid pooling (ASSP) is introduced to obtain cell image information features from each layer through different receptive domains. Finally, the multi-sided output fusion (MSOF) strategy is utilized to fuse the features of different semantic levels, so as to improve the accuracy of target segmentation. Experimental results on blood cell images for segmentation and classification (BCISC) dataset show that the proposed method has significant improvement in Matthew's correlation coefficient (Mcc), Dice and Jaccard values, which are better than the classical semantic segmentation network.

[Cloning and expression of fusion gene of transmembrane domain of human CD20 and g3pN in Escherichia coli].

AIM: To construct the expression vector containing transmembrane domain gene of human CD20 and g3pN1 gene and express the fusion gene high-efficiently in E.coli. METHODS: The human CD20 gene and g3pN1 domain gene were amplified by RT-PCR and PCR from Daudi cells and M13K07 phage antibody library, respectively, and then cloned into expression vector pTIG-Trx. The constructed expression vector was expressed in E.coli. RESULTS: Western blot analysis showed that expressed product could bind to anti-CD20 mAb. CONCLUSION: The pTIG-GS has been constructed and expressed successfully in E.coli, which lays the foundation for further screening anti-CD20 antibody from phage antibody library.