Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Net

05/21/2021
by   Julio Silva-Rodríguez, et al.
0

Prostate cancer is one of the most prevalent cancers worldwide. One of the key factors in reducing its mortality is based on early detection. The computer-aided diagnosis systems for this task are based on the glandular structural analysis in histology images. Hence, accurate gland detection and segmentation is crucial for a successful prediction. The methodological basis of this work is a prostate gland segmentation based on U-Net convolutional neural network architectures modified with residual and multi-resolution blocks, trained using data augmentation techniques. The residual configuration outperforms in the test subset the previous state-of-the-art approaches in an image-level comparison, reaching an average Dice Index of 0.77.

READ FULL TEXT
research
05/22/2020

Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net

Worldwide, prostate cancer is one of the main cancers affecting men. The...
research
06/17/2022

TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation

Colorectal cancer (CRC) is one of the most common causes of cancer and c...
research
05/30/2018

RUN:Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection

The early detection and early diagnosis of lung cancer are crucial to im...
research
11/10/2022

Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification

Glaucoma is a prevalent cause of blindness worldwide. If not treated pro...
research
03/16/2021

Colorectal Cancer Segmentation using Atrous Convolution and Residual Enhanced UNet

Colorectal cancer is a leading cause of death worldwide. However, early ...
research
05/03/2022

Attention U-Net for Glaucoma Identification Using Fundus Image Segmentation

Glaucoma is a fatal and worldwide ocular disease that can result in irre...
research
08/10/2022

Multi-structure segmentation for renal cancer treatment with modified nn-UNet

Renal cancer is one of the most prevalent cancers worldwide. Clinical si...

Please sign up or login with your details

Forgot password? Click here to reset