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EnglishAutomatic segmentation of the prostate on Magnetic Resonance Imaging (MRI) is one of the topics on which research has focused in recent years as it is a fundamental first step in the building process of a Computer aided diagnosis (CAD) system for cancer detection. Unfortunately, MRI acquired in different centers with different scanners leads to images with different characteristics. In this work, we propose an automatic algorithm for prostate segmentation, based on a U-Net applying transfer learning method in a bi-center setting. First, T2w images with and without endorectal coil from 80 patients acquired at Center A were used as training set and internal validation set. Then, T2w images without endorectal coil from 20 patients acquired at Center B were used as external validation. The reference standard for this study was manual segmentation of the prostate gland performed by an expert operator. The results showed a Dice similarity coefficient >85% in both internal and external validation datasets.Clinical Relevance- This segmentation algorithm could be integrated into a CAD system to optimize computational effort in prostate cancer detection.
Deep learning model for automatic prostate segmentation on bicentric T2w images with and without endorectal coil / Barra, D; Nicoletti, G; Defeudis, A; Mazzetti, S; Panic, J; Gatti, M; Faletti, R; Russo, F; Regge, D; Giannini, V. - ELETTRONICO. - 2021:(2021), pp. 3370-3373. ((Intervento presentato al convegno 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) tenutosi a Mexico nel 1-5 Nov. 2021 [10.1109/embc46164.2021.9630792].
| Titolo: | Deep learning model for automatic prostate segmentation on bicentric T2w images with and without endorectal coil | |
| Autori: | ||
| Data di pubblicazione: | 2021 | |
| Serie: | ||
| Abstract: | Automatic segmentation of the prostate on Magnetic Resonance Imaging (MRI) is one of the topics o...n which research has focused in recent years as it is a fundamental first step in the building process of a Computer aided diagnosis (CAD) system for cancer detection. Unfortunately, MRI acquired in different centers with different scanners leads to images with different characteristics. In this work, we propose an automatic algorithm for prostate segmentation, based on a U-Net applying transfer learning method in a bi-center setting. First, T2w images with and without endorectal coil from 80 patients acquired at Center A were used as training set and internal validation set. Then, T2w images without endorectal coil from 20 patients acquired at Center B were used as external validation. The reference standard for this study was manual segmentation of the prostate gland performed by an expert operator. The results showed a Dice similarity coefficient >85% in both internal and external validation datasets.Clinical Relevance- This segmentation algorithm could be integrated into a CAD system to optimize computational effort in prostate cancer detection. | |
| ISBN: | 978-1-7281-1179-7 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| Deep_learning_model_for_automatic_prostate_segmentation_on_bicentric_T2w_images_with_and_without_endorectal_coil.pdf | Conference proceeding | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
| Barra_EMBC2021.pdf | Conference proceeding | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2952164
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