A clinically informed automated evaluation pipeline for medical image segmentation based on Medical Similarity Index.

Szuzina Fazekas, Bettina K Budai, Viktor Bérczi, Pál Maurovich Horvat, Zsolt Vizi

Abstract read

In one paragraph

Article in Physics and imaging in radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Szuzina FazekasSemmelweis University, Medical Imaging Centre, Üllői str. 78a., 1085 Budapest, Hungary.

Bettina K BudaiSemmelweis University, Medical Imaging Centre, Üllői str. 78a., 1085 Budapest, Hungary.

Viktor BércziSemmelweis University, Medical Imaging Centre, Üllői str. 78a., 1085 Budapest, Hungary.

Pál Maurovich HorvatSemmelweis University, Medical Imaging Centre, Üllői str. 78a., 1085 Budapest, Hungary.

Zsolt ViziSemmelweis University, Medical Imaging Centre, Üllői str. 78a., 1085 Budapest, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate tissue delineation is essential in radiotherapy; however, conventional segmentation metrics mainly quantify geometric overlap and lack clinical interpretability. We proposed an automated Python-based evaluation pipeline using a bidirectional local distance-based metric that pairs test and reference contour points after center-of-mass correction and computes a similarity score from averaged Euclidean distances. The framework supports multislice images, multiple masks per slice, and concave mask separation, with open-source code provided. The method was demonstrated on fibroid and prostate MRI datasets, using 233 training cases and 12 test cases. In test examples, overlap scores exceeded 0.90, while Medical Similarity Index scores decreased to approximately 0.40.

Indexed as

Evaluation metricsImage processing pipelineImage segmentationMedical Similarity IndexRelevant metrics

Identifiers

PMID41971154

DOI10.1016/j.phro.2026.100950

PMCPMC13068865

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.