Automated Lesion Detection in Endoscopic Imagery for Small Animal Models

Eixelberger T, Fang Q, Zohud B, Hackner R, Jackstadt R, Stürzl M, Naschberger E, Wittenberg T (2024)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Journal

Publisher: Springer Vieweg

Series: Informatik aktuell

City/Town: Wiesbaden

Pages Range: 190-195

Conference Proceedings Title: Bildverarbeitung für die Medizin 2024. BVM 2024

Event location: Erlangen

ISBN: 9783658440367

DOI: 10.1007/978-3-658-44037-4_54

Abstract

Murine animal models are routinely used in research of gastrointestinal diseases, for example to analyze colorectal cancer or chronic inflammatory bowel diseases. By using suitable (miniaturized) endoscopy systems, it is possible to examine the large intestine of mice with respect to inflammatory, vascular or neoplastic changes without the need to sacrifice the animals. This enables the acquisition of high-resolution colonoscopy image sequences that can be used for the visual examination of tumors, the assessment of inflammation or the vasculature. Since the human resources for analyzing a multitude of videos, are limited, an automated evaluation of such image data is desirable. Video recordings (n = 49) of mice with and without colorectal cancer (CRC) were employed for this purpose and scored by clinical experts. The videos contained mice with tumors in 33 cases and 16 are pathologically normal. For the automatic detection of neoplastic changes (e.g. polyps), a deep neural network based on the YOLOv7- tiny architecture was applied. This network was previously trained on >36,000 human colon images with neoplasias visible in all frames. On test data with human images, the precision of the network is Prec = 0.92, and Rec = 0.90. The network was applied to the mouse data without any changes. To avoid falsepositive detections a color-based method was added to differentiate between stool residues and polyps. With the framework for the detection of neoplastic changes and classification of stool residues, we achieve results of Prec = 0.90, Rec = 0.98, F1 score = 0.94.Without the detection of stool residues, the values were dropping to Prec = 0.65 and Rec = 0.98, as 19 occurrences of stool are incorrectly classified as tumors. Our network trained on human data for neoplasia detection is able to accurately detect tumors in the murine colon. An additional module for the separation of stool residues is essential to avoid integration of wrongly positive cases.

Authors with CRIS profile

Involved external institutions

Deutsches Krebsforschungszentrum (DKFZ) Germany (DE)

How to cite

APA:

Eixelberger, T., Fang, Q., Zohud, B., Hackner, R., Jackstadt, R., Stürzl, M.,... Wittenberg, T. (2024). Automated Lesion Detection in Endoscopic Imagery for Small Animal Models. In Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024. BVM 2024 (pp. 190-195). Erlangen, DE: Wiesbaden: Springer Vieweg.

MLA:

Eixelberger, Thomas, et al. "Automated Lesion Detection in Endoscopic Imagery for Small Animal Models." Proceedings of the German Conference on Medical Image Computing, BVM 2024, Erlangen Ed. Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Wiesbaden: Springer Vieweg, 2024. 190-195.

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