Leveraging Self-supervised Denoising for Image Segmentation

Autor:	Prakash, Mangal, Buchholz, Tim-Oliver, Lalit, Manan, Tomancak, Pavel, Jug, Florian, Krull, Alexander
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: accepted at ISBI 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.12239 Zobrazit plný text záznamu View this record from Arxiv