Leveraging Self-supervised Denoising for Image Segmentation

2019/11/27 by Mangal Prakash, Prakash, Mangal, Tim-Oliver Buchholz +9 · 1 citation
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1911.12239

accepted at ISBI 2020

arxiv created 2020/03/19 · arxiv updated 2020/03/20

Abstract

Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy images. However, DL typically needs copious amounts of annotated training data that is for biomedical projects typically not available and excessively expensive to generate. Additionally, tasks become harder in the presence of noise, requiring even more high-quality training data. Hence, we propose to use denoising networks to improve the performance of other DL-based image segmentation methods. More specifically, we present ideas on how state-of-the-art self-supervised CARE networks can improve cell/nuclei segmentation in microscopy data. Using two state-of-the-art baseline methods, U-Net and StarDist, we show that our ideas consistently improve the quality of resulting segmentations, especially when only limited training data for noisy micrographs are available.

Cited by

• Self-Supervised Learning for Image Segmentation: A Comprehensive Survey

Related

• DenoiSeg: Joint Denoising and Segmentation
• Self-Supervised Versus Supervised Training for Segmentation of Organoid Images
• Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation
• Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images
• U-Net: Convolutional Networks for Biomedical Image Segmentation