An Auxiliary Task for Learning Nuclei Segmentation in 3D Microscopy Images

02/07/2020
by   Peter Hirsch, et al.
0

Segmentation of cell nuclei in microscopy images is a prevalent necessity in cell biology. Especially for three-dimensional datasets, manual segmentation is prohibitively time-consuming, motivating the need for automated methods. Learning-based methods trained on pixel-wise ground-truth segmentations have been shown to yield state-of-the-art results on 2d benchmark image data of nuclei, yet a respective benchmark is missing for 3d image data. In this work, we perform a comparative evaluation of nuclei segmentation algorithms on a database of manually segmented 3d light microscopy volumes. We propose a novel learning strategy that boosts segmentation accuracy by means of a simple auxiliary task, thereby robustly outperforming each of our baselines. Furthermore, we show that one of our baselines, the popular three-label model, when trained with our proposed auxiliary task, outperforms the recent StarDist-3D. As an additional, practical contribution, we benchmark nuclei segmentation against nuclei detection, i.e. the task of merely pinpointing individual nuclei without generating respective pixel-accurate segmentations. For learning nuclei detection, large 3d training datasets of manually annotated nuclei center points are available. However, the impact on detection accuracy caused by training on such sparse ground truth as opposed to dense pixel-wise ground truth has not yet been quantified. To this end, we compare nuclei detection accuracy yielded by training on dense vs. sparse ground truth. Our results suggest that training on sparse ground truth yields competitive nuclei detection rates.

READ FULL TEXT

page 2

page 4

page 7

page 8

page 11

research
05/09/2023

Self-supervised dense representation learning for live-cell microscopy with time arrow prediction

State-of-the-art object detection and segmentation methods for microscop...
research
12/05/2021

End-to-End Segmentation via Patch-wise Polygons Prediction

The leading segmentation methods represent the output map as a pixel gri...
research
10/13/2022

Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps

Specular microscopy assessment of the human corneal endothelium (CE) in ...
research
04/09/2019

QANet - Quality Assurance Network for Microscopy Cell Segmentation

Tools and methods for automatic image segmentation are rapidly developin...
research
09/03/2018

Learning Saliency Prediction From Sparse Fixation Pixel Map

Ground truth for saliency prediction datasets consists of two types of m...
research
05/02/2022

Leaf Tar Spot Detection Using RGB Images

Tar spot disease is a fungal disease that appears as a series of black c...
research
07/30/2019

2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy

Collagen fiber orientations in bones, visible with Second Harmonic Gener...

Please sign up or login with your details

Forgot password? Click here to reset