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 drop** 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chen C, Neumann J, Kühn F, Lee SM, Drefs M, Andrassy J et al. Establishment of an endoscopy-guided minimally invasive orthotopic mouse model of colorectal cancer. Cancers (Basel). 2020;12(10):3007.
Rosenberg DW, Giardina C, Tanaka T. Mouse models for the study of colon carcinogenesis. Carcinogenesis. 2009;30(2):183–96.
Taketo MM, Edelmann W. Mouse models of colon cancer. Gastroent. 2009;136(3):780–98.
Becker C, Fantini MC, Neurath MF. High resolution colonoscopy in live mice. Nat Protoc. 2006;1(6):2900–4.
Becker C, Fantini MC, Wirtz S, Nikolaev A, Kiesslich R, Lehr HA et al. In vivo imaging of colitis and colon cancer development in mice using high resolution chromoendoscopy. Gut. 2005;54(7):950–4.
Wittenberg T, Raithel M. Artificial intelligence-based polyp detection in colonoscopy: where have we been, where do we stand, and where are we headed? Visc Med. 2020;36(6):428–38.
Krenzer A, Banck M, Makowski K, Hekalo A, Fitting D, Troya J et al. A real-time polypdetection system with clinical application in colonoscopy using deep convolutional neural networks. J Imaging. 2023;9(2).
Ghose P, Ghose A, Sadhukhan D, Pal S, Mitra M. Improved polyp detection from colonoscopy images using finetuned YOLO-v5. Multimed Tools Appl. 2023.
Wang CY, Bochkovskiy A, Liao HYM. YOLOv7: trainable bag-of-freebies sets new stateof- the-art for real-time object detectors. 2022.
Ma Y, Chen X, Cheng K, Li Y, Sun B. LDPolypVideo benchmark: a large-scale colonoscopy video dataset of diverse polyps. Proc MICCAI. 2021:387–96.
Eixelberger T, Wolkenstein G, Hackner R, Bruns V, Mühldorfer S, Geissler U et al. YOLO networks for polyp detection: a human-in-the-loop training approach. Curr Dir Biomed Eng. 2022;8(2):277–80.
Bewley A, Ge Z, Ott L, Ramos F, Upcroft B. Simple online and realtime tracking. Processing IEEE. 2016:3464–8.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Eixelberger, T. et al. (2024). Automated Lesion Detection in Endoscopic Imagery for Small Animal Models. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_54
Download citation
DOI: https://doi.org/10.1007/978-3-658-44037-4_54
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-44036-7
Online ISBN: 978-3-658-44037-4
eBook Packages: Computer Science and Engineering (German Language)