Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection

Abstract

As a typical biomedical detection task, nuclei detection has been widely used in human health management, disease diagnosis and other fields. However, the task of cell detection in microscopic images is still challenging because the nuclei are commonly small and dense with many overlapping nuclei in the images. In order to detect nuclei, the most important key step is to segment the cell targets accurately. Based on Mask RCNN model, we designed a multi-path dilated residual network, and realized a network structure to segment and detect dense small objects, and effectively solved the problem of information loss of small objects in deep neural network. The experimental results on two typical nuclear segmentation data sets show that our model has better recognition and segmentation capability for dense small targets.

Suitability of Deep Weakly Supervised Learning to detect Acute Ischemic Stroke and Hemorrhagic Infarction Lesions Using Diffusion-weighted Imaging

“…ResNet can make the network much deeper and avoid the vanishing gradient problem. [24] Instead of simply stacking more convolution layers, many skip connections are added between the layers. Such a structure allows the gradient to pass backward through the skip connections, and all the convolution layers are able to be updated to extract features after the first training epoch.…”

A bird’s-eye view of deep learning in bioimage analysis

“…In contrast with traditional object detection methods, which have found broad application in bioimage analysis for spotting intracellular particles [16] , [18] , [210] , [211] , cell nuclei [17] , [26] , and cellular events such as mitosis [212] , [213] , [214] , deep learning approaches for these tasks have been explored since only recently. First results are promising [196] , [215] , [216] , [217] , [218] ( Fig. 4 B) but more extensive evaluations are needed to assess their general superiority.…”

A Fast and Accurate Algorithm for Nuclei Instance Segmentation in Microscopy Images

“…Several approaches, as exemplified by SSD [16] and RetinaNet [30], which eschewed the second refinement stage in two-stages methods and focused on direct sliding-window prediction, and shown promising results. For nuclei instance segmentation, Wang et al [31] designed a dilated residual network to solve the problem of information loss of small objects in deep neural network, and showed a well recognition and segmentation capability for nuclei detection and segmentation in microscopic images. In order to improve the precision of neural cell instance segmentation, Yi et al [15] constructed a joint network based on the SSD and Unet.…”