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Arabian Journal for Science and Engineering

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10.01.2022 | Research Article-Computer Engineering and Computer Science

Prostate Segmentation via Dynamic Fusion Model

verfasst von: Hakan Ocal, Necaattin Barisci

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 8/2022

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Abstract

Nowadays, many different methods are used in diagnosing prostate cancer. Among these methods, MRI-based imaging methods provide more precise information than other methods by obtaining the prostate's image from different angles (axial, sagittal, coronal). However, manually segmenting these images is very time-consuming and laborious. Besides, another challenge is the inhomogeneous and inconsistent appearance around the prostate borders, which is essential for cancer diagnosis. Nowadays, scientists are working intensively on deep learning-based techniques to identify prostate boundaries more efficiently and with high accuracy. In this study, a dynamic fusion architecture is proposed. For the fusion model, the Unet + Resnet3D and Unet + Resnet2D models were fused. Evaluation experiments were performed on the MICCAI 2012 Prostate Segmentation Challenge Dataset (PROMISE12) and the NCI-ISBI 2013(NCI_ISBI-13) Prostate Segmentation Challenge Dataset. Comparative analyzes show that the advantages and robustness of our method are superior to state-of-the-art approaches.

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Titel: Prostate Segmentation via Dynamic Fusion Model
verfasst von: Hakan Ocal
Necaattin Barisci
Publikationsdatum: 10.01.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Arabian Journal for Science and Engineering / Ausgabe 8/2022
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-021-06502-w

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