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

General

This dataset consists out of multiple Second Harmonic Generation (SHG) microscopy scans of collagen fibers in mice bones. Some mices are diseased with osteogenesis imperfecta (brittle bone).

We used this data to investigate the segmentation of uncertain local collagen fiber orientations. The corresponding paper "2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy" is accepted at GCPR 2019.

Abstract

Collagen fiber orientations in bones, visible with Second Harmonic Generation (SHG) microscopy, represent the inner structure and its alteration due to influences like cancer. While analyses of these orientations are valuable for medical research, it is not feasible to analyze the needed large amounts of local orientations manually. Since we have uncertain borders for these local orientations only rough regions can be segmented instead of a pixel-wise segmentation. We analyze the effect of these uncertain borders on human performance by a user study. Furthermore, we compare a variety of 2D and 3D methods such as classical approaches like Fourier analysis with state-of-the-art deep neural networks for the classification of local fiber orientations. We present a general way to use pretrained 2D weights in 3D neural networks, such as Inception-ResNet-3D a 3D extension of Inception-ResNet-v2. In a 10 fold cross-validation our two stage segmentation based on Inception-ResNet-3D and transferred 2D ImageNet weights achieves a human comparable accuracy.

Links

A preprint of the paper is available at https://arxiv.org/abs/1907.12868.

The final publication is available at Springer via https://doi.org/10.1007/978-3-030-33676-9_26

The source code is available at https://github.com/Emprime/uncertain-fiber-segmentation.

Data description

Please read the accompanying paper for more information about the dataset. Please see the source code for more information about the usage of the data.

• shg-ce-de:  contains the enhanced and denoised scans as image slices, the scans are sorted by mice (wt wildtyp, het ill mice), scan location and individual scan
• shg-masks: contains the ground truth masks for the three different classes (similar - Green, dissimilar - Red, not of interest - blue)
• shg-featues: contains the input and gt for the second stage of the proposed two stage segmentation
• shg-cross-splits: contains the 10 random splits for the 10 fold cross validation
• logs-prediction: contains the 10 tensorboard logs, weights and predictions for the 10 fold cross validations