Boundary overlap for medical image segmentation evaluation
All medical image segmentation algorithms need to be validated and compared, and yet no evaluation framework is widely accepted within the imaging community. Collections of segmentation results often need to be compared and ranked by their effectiveness. Evaluation measures which are popular in the...
| Egile Nagusiak: | , |
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| Formatua: | Conference item |
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Society of Photo-optical Instrumentation Engineers
2017
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| _version_ | 1826288585293168640 |
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| author | Voiculescu, I Yeghiazaryan, V |
| author_facet | Voiculescu, I Yeghiazaryan, V |
| author_sort | Voiculescu, I |
| collection | OXFORD |
| description | All medical image segmentation algorithms need to be validated and compared, and yet no evaluation framework is widely accepted within the imaging community. Collections of segmentation results often need to be compared and ranked by their effectiveness. Evaluation measures which are popular in the literature are based on region overlap or boundary distance. None of these are consistent in the way they rank segmentation results: they tend to be sensitive to one or another type of segmentation error (size, location, shape) but no single measure covers all error types. We introduce a new family of measures, with hybrid characteristics. These measures quantify similarity/difference of segmented regions by considering their overlap around the region boundaries. This family is more sensitive than other measures in the literature to combinations of segmentation error types. We compare measure performance on collections of segmentation results sourced from carefully compiled 2D synthetic data, and also on 3D medical image volumes. We show that our new measure (1) penalises errors successfully, especially those around region boundaries; (2) gives a low similarity score when existing measures disagree, thus avoiding overly inflated scores; and (3) scores segmentation results over a wider range of values. We consider a representative measure from this family and the effect of its only free parameter on error sensitivity, typical value range, and running time. |
| first_indexed | 2024-03-07T02:15:55Z |
| format | Conference item |
| id | oxford-uuid:a23c2573-e946-40e7-9800-49de700db860 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T02:15:55Z |
| publishDate | 2017 |
| publisher | Society of Photo-optical Instrumentation Engineers |
| record_format | dspace |
| spelling | oxford-uuid:a23c2573-e946-40e7-9800-49de700db8602022-03-27T02:18:45ZBoundary overlap for medical image segmentation evaluationConference itemhttp://purl.org/coar/resource_type/c_5794uuid:a23c2573-e946-40e7-9800-49de700db860Symplectic Elements at OxfordSociety of Photo-optical Instrumentation Engineers2017Voiculescu, IYeghiazaryan, VAll medical image segmentation algorithms need to be validated and compared, and yet no evaluation framework is widely accepted within the imaging community. Collections of segmentation results often need to be compared and ranked by their effectiveness. Evaluation measures which are popular in the literature are based on region overlap or boundary distance. None of these are consistent in the way they rank segmentation results: they tend to be sensitive to one or another type of segmentation error (size, location, shape) but no single measure covers all error types. We introduce a new family of measures, with hybrid characteristics. These measures quantify similarity/difference of segmented regions by considering their overlap around the region boundaries. This family is more sensitive than other measures in the literature to combinations of segmentation error types. We compare measure performance on collections of segmentation results sourced from carefully compiled 2D synthetic data, and also on 3D medical image volumes. We show that our new measure (1) penalises errors successfully, especially those around region boundaries; (2) gives a low similarity score when existing measures disagree, thus avoiding overly inflated scores; and (3) scores segmentation results over a wider range of values. We consider a representative measure from this family and the effect of its only free parameter on error sensitivity, typical value range, and running time. |
| spellingShingle | Voiculescu, I Yeghiazaryan, V Boundary overlap for medical image segmentation evaluation |
| title | Boundary overlap for medical image segmentation evaluation |
| title_full | Boundary overlap for medical image segmentation evaluation |
| title_fullStr | Boundary overlap for medical image segmentation evaluation |
| title_full_unstemmed | Boundary overlap for medical image segmentation evaluation |
| title_short | Boundary overlap for medical image segmentation evaluation |
| title_sort | boundary overlap for medical image segmentation evaluation |
| work_keys_str_mv | AT voiculescui boundaryoverlapformedicalimagesegmentationevaluation AT yeghiazaryanv boundaryoverlapformedicalimagesegmentationevaluation |