at Google ScholarTitle data
Zaha, Paul ; Allmendinger, Simeon ; Böcking, Lars ; Müller, Leopold ; Kühl, Niklas:
Do Edges Matter? Investigating Edge-Enhanced Pre-Training for Medical Image Segmentation.
In:
Data Engineering in Medical Imaging : Third MICCAI Workshop, DEMI 2025, Held in Conjunction with MICCAI 2025 ; Proceedings. -
Cham
: Springer
,
2025
. - pp. 64-74
. - (Lecture Notes in Computer Science
; 16191
)
ISBN 978-3-032-08008-0
DOI: https://doi.org/10.1007/978-3-032-08009-7_7
Abstract in another language
Medical image segmentation is crucial for disease diagnosis and treatment planning, yet developing robust segmentation models often requires substantial computational resources and large datasets. Existing research shows that foundation models, trained on broad sets of image data and subsequently fine-tuned for specific medical tasks, can boost segmentation performance. However, questions remain about how particular image preprocessing steps may influence segmentation performance across different medical imaging modalities. In particular, edges---abrupt transitions in pixel intensity---are widely acknowledged as vital cues for object boundaries but have not been systematically examined in the pre-training of foundation models. We address this gap by investigating to which extend pre-training with data processed using computationally efficient edge enhancement kernels, such as kirsch, can improve cross-modality segmentation capabilities of a foundation model. Two versions of a foundation model are first trained on either raw or edge-enhanced data across multiple medical imaging modalities, then fine-tuned on selected raw subsets tailored to specific medical modalities. After systematic investigation using the medical domains Dermoscopy, Fundus, Mammography, Microscopy, OCT, US, and XRay, we discover both increased and reduced segmentation performance across modalities using edge-focused pre-training, indicating the need for a selective application of this approach. To guide such selective applications, we propose a meta-learning strategy. It uses standard deviation and image entropy of the raw image to choose between a model pre-trained on edge-enhanced or on raw data for optimal performance. Our experiments show that integrating this meta-learning layer yields an overall segmentation performance improvement across diverse medical imaging tasks by 16.42\% compared to models pre-trained on edge-enhanced data only and 19.30\% compared to models pre-trained on raw data only.