Cutaneous tumors, i.e., tumors originating from skin cells, are one of the most common tumor types in dogs [1]. As cost efficiency is an important driver in animal care, it would be strongly beneficial to support veterinary pathologists in the diagnosis of those tumors and their respective subtypes. Besides the use in a decision-support system, computerized segmentation and classification of tumors can potentially increase precision for therapeutic options and – through quantitative evaluation – provide new insights into tumor development with inter-species relevance, which includes humans.
This thesis aims to perform tissue segmentation from a data set comprising of the nine most common canine cutaneous tumor types of whole slide images. A particularly challenging component is the combination of predictions performed for differing magnifications: while some tissue types can be spotted with higher accuracy using lower magnifications, for others human experts will utilize higher magnifications, especially for fine-grained differentiation versus neighboring tissue segments.
The thesis comprises the following items:
- Literature review concerning detection on multi-scale images
- Training of state-of-the-art segmentation networks on multiple scales of microscopy images
- Analysis of detection results on single scales
- Development of a multi-scale fusion system, achieving high precision at multiple image scales
- Documentation and presentation of the findings, documentation of code
[1] Murphy, S. (2006). Skin neoplasia in small animals 3. Common canine tumours. In practice, 28(7), 398-402.