Registration of serial sections: An evaluation method based on distortions of the ground truths

TL;DR

This paper introduces a ground-truth-based evaluation method for histological serial section registration, using simulated distortions on 3D data to assess registration accuracy and compare multiple methods.

Contribution

It proposes a novel methodology to generate test data with known distortions for validating registration algorithms, enabling precise evaluation of registration performance.

Findings

Evaluation across six registration methods shows varying accuracy.

Distorted and ground truth datasets are publicly available.

The method effectively identifies over- and under-registration issues.

Abstract

Registration of histological serial sections is a challenging task. Serial sections exhibit distortions and damage from sectioning. Missing information on how the tissue looked before cutting makes a realistic validation of 2D registrations extremely difficult. This work proposes methods for ground-truth-based evaluation of registrations. Firstly, we present a methodology to generate test data for registrations. We distort an innately registered image stack in the manner similar to the cutting distortion of serial sections. Test cases are generated from existing 3D data sets, thus the ground truth is known. Secondly, our test case generation premises evaluation of the registrations with known ground truths. Our methodology for such an evaluation technique distinguishes this work from other approaches. Both under- and over-registration become evident in our evaluations. We also survey existing validation efforts. We present a full-series evaluation across six different registration methods applied to our distorted 3D data sets of animal lungs. Our distorted and ground truth data sets are made publicly available.