Dual U-Net for the Segmentation of Overlapping Glioma Nuclei
The morphology and surroundings of cells have been routinely used by pathologists to diagnose the pathological subtypes of gliomas and to assess the malignancy of tumors. Thanks to the advent and development of digital pathology technology, it is possible to automatically analyze whole slides of tis...
Main Authors: | Xieli Li, Yuanyuan Wang, Qisheng Tang, Zhen Fan, Jinhua Yu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8744511/ |
Similar Items
-
Microscopy cell nuclei segmentation with enhanced U-Net
by: Feixiao Long
Published: (2020-01-01) -
RIC-Unet: An Improved Neural Network Based on Unet for Nuclei Segmentation in Histology Images
by: Zitao Zeng, et al.
Published: (2019-01-01) -
Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection
by: Eric Ke Wang, et al.
Published: (2019-05-01) -
Promising Deep Semantic Nuclei Segmentation Models for Multi-Institutional Histopathology Images of Different Organs
by: Loay Hassan, et al.
Published: (2021-05-01) -
Enhancing Multi-tissue and Multi-scale Cell Nuclei Segmentation with Deep Metric Learning
by: Tomas Iesmantas, et al.
Published: (2020-01-01)