Superpixel Pre-Segmentation of HER2 Slides for Efficient Annotation. (arXiv:2201.07572v1 [cs.CV])

Supervised deep learning has shown state-of-the-art performance for medical
image segmentation across different applications, including histopathology and
cancer research; however, the manual annotation of such data is extremely
laborious. In this work, we explore the use of superpixel approaches to compute
a pre-segmentation of HER2 stained images for breast cancer diagnosis that
facilitates faster manual annotation and correction in a second step. Four
methods are compared: Standard Simple Linear Iterative Clustering (SLIC) as a
baseline, a domain adapted SLIC, and …

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