Search

Accurate segmentation of nuclei in histology images is essential for digital pathology. However, previous work should have considered integrating nuclei contour information into network learning. The main problems are (1) nuclei contour information needs to be fully considered, resulting in inaccurate spatial location of nuclei. (2) Small nuclei in dense regions or irregularly shaped nuclei cannot be segmented. (3) Mainstream networks consider more long-distance semantic information and larger receptive fields and do not consider the fusion of feature maps with different semantics. To solve the above problems, we have proposed a contour-based dual-path instance segmentation network. Firstly we reconstructed the nuclei contour information using algorithms and morphological manipulations. Provide input for subsequent contouring networks. Then we designed a dual-path network. It can extract nuclei features and contour features independently in the encoding stage and fuse the feature maps at different scales in the decoding stage. In the decoding stage, we use the attention module with a newly designed fusion mechanism, which allows us to fuse different semantics of feature maps for simple and efficient fusion. Finally, using the watershed algorithm, we use the network segmentation results to get the instance segmentation results of the nuclei. We used four publicly available datasets, including Kumar, CPM-15, CPM-17, and TNBC, which contain the nuclei of many organs and different forms of nuclei in the human body. Compared with the mainstream methods, we obtained the best AJI metrics, which are 0.656,0.717, and 0.602. In conclocess can effectively improve the performance of the nuclei instance segmentation network. It can accurately locate the scattered nuclei in space with small segmented nuclei in dense regions. [ABSTRACT FROM AUTHOR]

In the medical field, hematoxylin and eosin (H&E)-stained histopathology images of cell nuclei analysis represent an important measure for cancer diagnosis. The most valuable aspect of the nuclei analysis is the segmentation of the different nuclei morphologies of different organs and subsequent diagnosis of the type and severity of the disease based on pathology. In recent years, deep learning techniques have been widely used in digital histopathology analysis. Automated nuclear segmentation technology enables the rapid and efficient segmentation of tens of thousands of complex and variable nuclei in histopathology images. However, a challenging problem during nuclei segmentation is the blocking of cell nuclei, overlapping, and background complexity of the tissue fraction. To address this challenge, we present MIU-net, an efficient deep learning network structure for the nuclei segmentation of histopathology images. Our proposed structure includes two blocks with modified inception module and attention module. The advantage of the modified inception module is to balance the computation and network performance of the deeper layers of the network, combined with the convolutional layer using different sizes of kernels to learn effective features in a fast and efficient manner to complete kernel segmentation. The attention module allows us to extract small and fine irregular boundary features from the images, which can better segment cancer cells that appear disorganized and fragmented. We test our methodology on public kumar datasets and achieve the highest AUC score of 0.92. The experimental results show that the proposed method achieves better performance than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]