Registration of Histopathogy Images Using Structural Information From Fine Grained Feature Maps
This addresses a practical limitation in clinical workflows where manual segmentations are often unavailable, though it appears incremental as it builds on existing registration and segmentation frameworks.
The paper tackles the problem of medical image registration when manual segmentation maps are unavailable by proposing a method that uses self-supervised segmentation feature maps extracted from a pre-trained network. The results show this approach effectively replaces manual segmentation and achieves state-of-the-art registration performance in real-world scenarios.
Registration is an important part of many clinical workflows and factually, including information of structures of interest improves registration performance. We propose a novel approach of combining segmentation information in a registration framework using self supervised segmentation feature maps extracted using a pre-trained segmentation network followed by clustering. Using self supervised feature maps enables us to use segmentation information despite the unavailability of manual segmentations. Experimental results show our approach effectively replaces manual segmentation maps and demonstrate the possibility of obtaining state of the art registration performance in real world cases where manual segmentation maps are unavailable.