Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods.
Abdom Radiol (NY)
; 49(5): 1545-1556, 2024 05.
Article
em En
| MEDLINE
| ID: mdl-38512516
ABSTRACT
OBJECTIVE:
Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters. MATERIALS ANDMETHODS:
A single institution retrospective database was queried for the following conditions at prostate MRI prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model.RESULTS:
683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05-329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707-0.971), 0.873 (0-0.997), and 0.894 (0.025-0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001).CONCLUSION:
Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Próstata
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Algoritmos
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Inteligência Artificial
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Imageamento por Ressonância Magnética
Limite:
Aged
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article