A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images
This paper addresses the task of nuclei segmentation in high-resolution histopathological images. We propose an auto- matic end-to-end deep neural network algorithm for segmenta- tion of individual nuclei. A nucleus-boundary model is introduced to predict nuclei and their boundaries simultaneously using a fully convolutional neural network. Given a color normalized image, the model directly outputs an estimated nuclei map and a boundary map. A simple, fast and parameter-free post-processing procedure is performed on the estimated nuclei map to produce the final segmented nuclei. An overlapped patch extraction and assembling method is also designed for seamless prediction of nuclei in large whole-slide images. We also show the effectiveness of data augmentation methods for nuclei segmentation task. Our experiments showed our method outperforms prior state-of-the- art methods. Moreover, it is efficient that one 1000X1000 image can be segmented in less than 5 seconds. This makes it possible to precisely segment the whole-slide image in acceptable time
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationSegmentationwhole slide imagesSimilar Papers 제목 키워드 기반
CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation
Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to extract rich features for cellular estimation and following diagnosis as well as treatment. While it still remains challenging be…
Instance SegmentationMulti-tissue Nucleus SegmentationSegmentationSemantic SegmentationEnhanced Sharp-GAN For Histopathology Image Synthesis
Histopathology image synthesis aims to address the data shortage issue in training deep learning approaches for accurate cancer detection. However, existing methods struggle to produce realistic images that have accurate…
Image GenerationSegmentationEnhancing Nucleus Segmentation with HARU-Net: A Hybrid Attention Based Residual U-Blocks Network
Nucleus image segmentation is a crucial step in the analysis, pathological diagnosis, and classification, which heavily relies on the quality of nucleus segmentation. However, the complexity of issues such as variations …
Image SegmentationInstance SegmentationSegmentationSemantic SegmentationBend-Net: Bending Loss Regularized Multitask Learning Network for Nuclei Segmentation in Histopathology Images
Separating overlapped nuclei is a major challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on nuclei segmentation; however, their performance on separati…
Instance SegmentationSegmentationSemantic SegmentationBending Loss Regularized Network for Nuclei Segmentation in Histopathology Images
Separating overlapped nuclei is a major challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on public datasets; however, their performance in segmenting o…