MOTH: Memory-Efficient On-the-Fly Tiling of Histological Image Annotations Using QuPath

• Published in: Journal of imaging Vol. 10; no. 11; p. 292
• Main Authors: Kauer, Thomas, Sehring, Jannik, Schmid, Kai, Bartkuhn, Marek, Wiebach, Benedikt, Crnkovic, Slaven, Kwapiszewska, Grazyna, Acker, Till, Amsel, Daniel
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.11.2024; MDPI
• Subjects: Algorithms; Annotations; Artificial intelligence; Automation; Comparative analysis; Computer programs; Data analysis; Datasets; digital pathology; Flexibility; Hard disks; Histology; Image annotation; Image processing; Machine learning; Methods; Neural networks; Performance evaluation; Polygons; Python; qupath; Science; segmentation; Technical Note; Technology application; Tiles; Tiling; Toolkits; Visual flight; whole slide image; Austria; qupath; segmentation; digital pathology; artificial intelligence; whole slide image
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging10110292

The emerging usage of digitalized histopathological images is leading to a novel possibility for data analysis. With the help of artificial intelligence algorithms, it is now possible to detect certain structures and morphological features on whole slide images automatically. This enables algorithms to count, measure, or evaluate those areas when trained properly. To achieve suitable training, datasets must be annotated and curated by users in programs like QuPath. The extraction of this data for artificial intelligence algorithms is still rather tedious and needs to be saved on a local hard drive. We developed a toolkit for integration into existing pipelines and tools, like U-net, for the on-the-fly extraction of annotation tiles from existing QuPath projects. The tiles can be directly used as input for artificial intelligence algorithms, and the results are directly transferred back to QuPath for visual inspection. With the toolkit, we created a convenient way to incorporate QuPath into existing AI workflows.