ArticleDigital health
Advancements in prostate cancer segmentation: Integrating prostate zonal information.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background and Objective: Prostate cancer is one of the most common cancers in men, and early diagnosis is critical. Segmentation of cancerous regions in multiparametric MRI is a key step. Deep neural networks, such as nnU-Net, perform well, and incorporating prostate zonal information may further improve accuracy. Methods: This study introduces four prostate cancer segmentation ensembles that integrate zonal data, compared with a baseline model, which uses zonal information as a separate input channel. Ensembles employ specific prostate zone cancer segmentation models trained with the nnU-Net method. To address variability in manual annotations, a new evaluation metric, the tolerant Dice Score Coefficient ( Results: Ensemble 3 yields the best performance, with a 4.77% higher mean DSC and 6.17% higher mean Conclusion: The experiment results indicate that the proposed Ensemble 4 is the most suitable solution for the prostate cancer segmentation task. Moreover, the results also indicate that the proposed metric,
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