InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation

06/30/2022
by   Yi Lin, et al.
0

Nuclei Segmentation from histology images is a fundamental task in digital pathology analysis. However, deep-learning-based nuclei segmentation methods often suffer from limited annotations. This paper proposes a realistic data augmentation method for nuclei segmentation, named InsMix, that follows a Copy-Paste-Smooth principle and performs morphology-constrained generative instance augmentation. Specifically, we propose morphology constraints that enable the augmented images to acquire luxuriant information about nuclei while maintaining their morphology characteristics (e.g., geometry and location). To fully exploit the pixel redundancy of the background and improve the model's robustness, we further propose a background perturbation method, which randomly shuffles the background patches without disordering the original nuclei distribution. To achieve contextual consistency between original and template instances, a smooth-GAN is designed with a foreground similarity encoder (FSE) and a triplet loss. We validated the proposed method on two datasets, i.e., Kumar and CPS datasets. Experimental results demonstrate the effectiveness of each component and the superior performance achieved by our method to the state-of-the-art methods.

READ FULL TEXT

page 2

page 8

page 11

page 12

research
11/27/2019

PanDA: Panoptic Data Augmentation

The recently proposed panoptic segmentation task presents a significant ...
research
04/01/2022

ObjectMix: Data Augmentation by Copy-Pasting Objects in Videos for Action Recognition

In this paper, we propose a data augmentation method for action recognit...
research
10/07/2022

Automated segmentation and morphological characterization of placental histology images based on a single labeled image

In this study, a novel method of data augmentation has been presented fo...
research
02/07/2023

3D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotations

Delineating 3D blood vessels is essential for clinical diagnosis and tre...
research
01/25/2021

Spatio-temporal Data Augmentation for Visual Surveillance

Visual surveillance aims to stably detect a foreground object using a co...
research
04/11/2021

SIGAN: A Novel Image Generation Method for Solar Cell Defect Segmentation and Augmentation

Solar cell electroluminescence (EL) defect segmentation is an interestin...

Please sign up or login with your details

Forgot password? Click here to reset