Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles
| العنوان: | Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles |
|---|---|
| المؤلفون: | Florian Kofler, Ivan Ezhov, Lucas Fidon, Carolin M. Pirkl, Johannes C. Paetzold, Egon Burian, Sarthak Pati, Malek El Husseini, Fernando Navarro, Suprosanna Shit, Jan Kirschke, Spyridon Bakas, Claus Zimmer, Benedikt Wiestler, Bjoern H. Menze |
| المساهمون: | University of Zurich, Kofler, Florian |
| المصدر: | Frontiers in Neuroscience Frontiers in Neuroscience, Vol 15 (2021) |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | fusion, quality estimation, General Neuroscience, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 2800 General Neuroscience, 610 Medicine & health, Neurosciences. Biological psychiatry. Neuropsychiatry, MR, anomaly detection, ddc, failure prediction, 11493 Department of Quantitative Biomedicine, ensembling, RC321-571, Neuroscience, Original Research, OOD, CT |
| الوصف: | A multitude of image-based machine learning segmentation and classification algorithms has recently been proposed, offering diagnostic decision support for the identification and characterization of glioma, Covid-19 and many other diseases. Even though these algorithms often outperform human experts in segmentation tasks, their limited reliability, and in particular the inability to detect failure cases, has hindered translation into clinical practice. To address this major shortcoming, we propose an unsupervised quality estimation method for segmentation ensembles. Our primitive solution examines discord in binary segmentation maps to automatically flag segmentation results that are particularly error-prone and therefore require special assessment by human readers. We validate our method both on segmentation of brain glioma in multi-modal magnetic resonance - and of lung lesions in computer tomography images. Additionally, our method provides an adaptive prioritization mechanism to maximize efficacy in use of human expert time by enabling radiologists to focus on the most difficult, yet important cases while maintaining full diagnostic autonomy. Our method offers an intuitive and reliable uncertainty estimation from segmentation ensembles and thereby closes an important gap toward successful translation of automatic segmentation into clinical routine. |
| وصف الملف: | 2021_Frontiers_in_Neurosc.pdf - application/pdf; application/pdf |
| تدمد: | 1662-4548 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e2ee667a833b0a4c28df7115613556ffTest https://pubmed.ncbi.nlm.nih.gov/35035351Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....e2ee667a833b0a4c28df7115613556ff |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 16624548 |
|---|