Regional registration of whole slide image stacks containing major histological artifacts.

BACKGROUND: High resolution 2D whole slide imaging provides rich information about the tissue structure. This information can be a lot richer if these 2D images can be stacked into a 3D tissue volume. A 3D analysis, however, requires accurate reconstruction of the tissue volume from the 2D image stack. This task is not trivial due to the distortions such as tissue tearing, folding and missing at each slide. Performing registration for the whole tissue slices may be adversely affected by distorted tissue regions. Consequently, regional registration is found to be more effective. In this paper, we propose a new approach to an accurate and robust registration of regions of interest for whole slide images. We introduce the idea of multi-scale attention for registration. RESULTS: Using mean similarity index as the metric, the proposed algorithm (mean ± SD [Formula: see text]) followed by a fine registration algorithm ([Formula: see text]) outperformed the state-of-the-art linear whole tissue registration algorithm ([Formula: see text]) and the regional version of this algorithm ([Formula: see text]). The proposed algorithm also outperforms the state-of-the-art nonlinear registration algorithm (original: [Formula: see text], regional: [Formula: see text]) for whole slide images and a recently proposed patch-based registration algorithm (patch size 256: [Formula: see text] , patch size 512: [Formula: see text]) for medical images. CONCLUSION: Using multi-scale attention mechanism leads to a more robust and accurate solution to the problem of regional registration of whole slide images corrupted in some parts by major histological artifacts in the imaged tissue.

Semi-automated quantitative Drosophila wings measurements.

BACKGROUND: Drosophila melanogaster is an important organism used in many fields of biological research such as genetics and developmental biology. Drosophila wings have been widely used to study the genetics of development, morphometrics and evolution. Therefore there is much interest in quantifying wing structures of Drosophila. Advancement in technology has increased the ease in which images of Drosophila can be acquired. However such studies have been limited by the slow and tedious process of acquiring phenotypic data. RESULTS: We have developed a system that automatically detects and measures key points and vein segments on a Drosophila wing. Key points are detected by performing image transformations and template matching on Drosophila wing images while vein segments are detected using an Active Contour algorithm. The accuracy of our key point detection was compared against key point annotations of users. We also performed key point detection using different training data sets of Drosophila wing images. We compared our software with an existing automated image analysis system for Drosophila wings and showed that our system performs better than the state of the art. Vein segments were manually measured and compared against the measurements obtained from our system. CONCLUSION: Our system was able to detect specific key points and vein segments from Drosophila wing images with high accuracy.