I implemented an image segmentation pipeline and I trained it on the DICOM dataset. I compared the results of the model with manual segmentation to find the accuracy. Is there other methods for evaluation?
Martin Thoma: A Survey of Semantic Segmentation, Section III
Subsection A is about metrics and B is about datasets.
Metrics include: accuracy, IoU, frequency weighted IoU, F-beta score, speed, ...
The paper referenced by Martin Thoma is the go-to for semantic segmentation. However I will also like to add the Panoptic Segmentation metric as an aggregated method to measure both the detection task and segmentation task of the model.
It is a very well-known and widely used metric since it is the standard metric for COCO dataset (segmentation)
This is the paper where the metric is proposed.
And here is the metric: