guest ::
| Home > Publications database > Comparative benchmarking of failure detection methods in medical image segmentation: Unveiling the role of confidence aggregation. > print |
| Home > Publications database > Comparative benchmarking of failure detection methods in medical image segmentation: Unveiling the role of confidence aggregation. > print |
| 001 | 294922 | ||
| 005 | 20241216111324.0 | ||
| 024 | 7 | _ | |a 10.1016/j.media.2024.103392 |2 doi |
| 024 | 7 | _ | |a pmid:39657400 |2 pmid |
| 024 | 7 | _ | |a 1361-8415 |2 ISSN |
| 024 | 7 | _ | |a 1361-8431 |2 ISSN |
| 024 | 7 | _ | |a 1361-8423 |2 ISSN |
| 037 | _ | _ | |a DKFZ-2024-02629 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Zenk, Maximilian |0 P:(DE-He78)eafef5cb69dd3d85f1cc942c474a220f |b 0 |e First author |u dkfz |
| 245 | _ | _ | |a Comparative benchmarking of failure detection methods in medical image segmentation: Unveiling the role of confidence aggregation. |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2025 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1734343984_24909 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a #EA:E230#LA:E230# / Available online 30 November 2024 |
| 520 | _ | _ | |a Semantic segmentation is an essential component of medical image analysis research, with recent deep learning algorithms offering out-of-the-box applicability across diverse datasets. Despite these advancements, segmentation failures remain a significant concern for real-world clinical applications, necessitating reliable detection mechanisms. This paper introduces a comprehensive benchmarking framework aimed at evaluating failure detection methodologies within medical image segmentation. Through our analysis, we identify the strengths and limitations of current failure detection metrics, advocating for the risk-coverage analysis as a holistic evaluation approach. Utilizing a collective dataset comprising five public 3D medical image collections, we assess the efficacy of various failure detection strategies under realistic test-time distribution shifts. Our findings highlight the importance of pixel confidence aggregation and we observe superior performance of the pairwise Dice score (Roy et al., 2019) between ensemble predictions, positioning it as a simple and robust baseline for failure detection in medical image segmentation. To promote ongoing research, we make the benchmarking framework available to the community. |
| 536 | _ | _ | |a 315 - Bildgebung und Radioonkologie (POF4-315) |0 G:(DE-HGF)POF4-315 |c POF4-315 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de |
| 650 | _ | 7 | |a Distribution shift |2 Other |
| 650 | _ | 7 | |a Failure detection |2 Other |
| 650 | _ | 7 | |a Quality control |2 Other |
| 650 | _ | 7 | |a Semantic segmentation |2 Other |
| 650 | _ | 7 | |a Uncertainty estimation |2 Other |
| 700 | 1 | _ | |a Zimmerer, David |0 P:(DE-He78)c1fcef80eab3d1e4fc187faece1a439c |b 1 |u dkfz |
| 700 | 1 | _ | |a Isensee, Fabian |0 P:(DE-He78)7ea9af59d03ec7deb982a0e0562358fa |b 2 |u dkfz |
| 700 | 1 | _ | |a Traub, Jeremias |0 P:(DE-He78)ee61463b02d49b2e085e3e7c8d6d963e |b 3 |u dkfz |
| 700 | 1 | _ | |a Norajitra, Tobias |0 P:(DE-He78)a70f21a2bf78bbc1306c3d432ae08dc7 |b 4 |u dkfz |
| 700 | 1 | _ | |a Jäger, Paul F |0 P:(DE-He78)04a0b5a49db132d8f00cee326cb743ca |b 5 |u dkfz |
| 700 | 1 | _ | |a Maier-Hein, Klaus |0 P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3 |b 6 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.1016/j.media.2024.103392 |g Vol. 101, p. 103392 - |0 PERI:(DE-600)1497450-2 |p 103392 |t Medical image analysis |v 101 |y 2025 |x 1361-8415 |
| 909 | C | O | |p VDB |o oai:inrepo02.dkfz.de:294922 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 0 |6 P:(DE-He78)eafef5cb69dd3d85f1cc942c474a220f |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 1 |6 P:(DE-He78)c1fcef80eab3d1e4fc187faece1a439c |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 2 |6 P:(DE-He78)7ea9af59d03ec7deb982a0e0562358fa |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 3 |6 P:(DE-He78)ee61463b02d49b2e085e3e7c8d6d963e |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 4 |6 P:(DE-He78)a70f21a2bf78bbc1306c3d432ae08dc7 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 5 |6 P:(DE-He78)04a0b5a49db132d8f00cee326cb743ca |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 6 |6 P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3 |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Krebsforschung |1 G:(DE-HGF)POF4-310 |0 G:(DE-HGF)POF4-315 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-300 |4 G:(DE-HGF)POF |v Bildgebung und Radioonkologie |x 0 |
| 914 | 1 | _ | |y 2024 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b MED IMAGE ANAL : 2022 |d 2023-10-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2023-10-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2023-10-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2023-10-21 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2023-10-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2023-10-21 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2023-10-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2023-10-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2023-10-21 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1160 |2 StatID |b Current Contents - Engineering, Computing and Technology |d 2023-10-21 |
| 915 | _ | _ | |a IF >= 10 |0 StatID:(DE-HGF)9910 |2 StatID |b MED IMAGE ANAL : 2022 |d 2023-10-21 |
| 920 | 2 | _ | |0 I:(DE-He78)E230-20160331 |k E230 |l E230 Medizinische Bildverarbeitung |x 0 |
| 920 | 1 | _ | |0 I:(DE-He78)E230-20160331 |k E230 |l E230 Medizinische Bildverarbeitung |x 0 |
| 920 | 1 | _ | |0 I:(DE-He78)E290-20160331 |k E290 |l NWG Interaktives maschinelles Lernen |x 1 |
| 920 | 0 | _ | |0 I:(DE-He78)E230-20160331 |k E230 |l E230 Medizinische Bildverarbeitung |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-He78)E230-20160331 |
| 980 | _ | _ | |a I:(DE-He78)E290-20160331 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|