A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI
| Autor: | Elise Sandsmark, Kirsten Margrete Selnæs, Olmo Zavala-Romero, Tone Frost Bathen, Mattijs Elschot, Gabriel Nketiah, Mohammed R. S. Sunoqrot, Radka Stoyanova |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Clinical Biochemistry CAD Article Standard deviation 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Approximation error Robustness (computer science) Linear regression computer-aided detection and diagnosis Segmentation quality control lcsh:R5-920 prostate business.industry Deep learning segmentation deep learning Pattern recognition radiomics MRI machine learning 030220 oncology & carcinogenesis Quality Score Artificial intelligence business lcsh:Medicine (General) |
| Zdroj: | Diagnostics (Basel) Diagnostics, Vol 10, Iss 714, p 714 (2020) Diagnostics; Volume 10; Issue 9; Pages: 714 Diagnostics |
| Popis: | Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
| Databáze: | OpenAIRE |
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