Automated Lesion Detection in Endoscopic Imagery for Small Animal Models

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.