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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?

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See:

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, ...

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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:

enter image description here

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