Automatic Detection of Histological Artifacts in Mouse Brain Slice Images

Abstract

A major challenge in automatic registration, alignment and 3-D reconstruction of conventionally processed mouse brain slice images is the presence of histological artifacts, like tissue tears and losses. These artifacts are often produced from manual sample preparation processes, which are ubiquitous in most neuroanatomical laboratories. We present a novel geometric algorithm to automatically detect these artifacts (damage regions) in mouse brain slice images. Our algorithm is guided by our observation that the tears and tissue loss in brain slice images result in external geometric medial axis of the outer contours to go deep inside the tissue. We tested our algorithm on 52 mouse brain slice images with major histological artifacts and successfully detected all the damage regions in the dataset. Our algorithm also demonstrated much lower errors when quantitatively evaluated by performing feature based registration between all 52 slices and their corresponding Allen Reference Atlas (ARA) images.