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Prostate Segmentation via Dynamic Fusion Model

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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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Hakan Ocal and Necaattin Barisci: Conceptualization, Validation, writing—review, and editing. Hakan Ocal: Methodology, Software, Data curation, Writing—Original draft preparation. Necaattin Barisci: Supervision, Visualization, Investigation. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Hakan Ocal.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Ocal, H., Barisci, N. Prostate Segmentation via Dynamic Fusion Model. Arab J Sci Eng 47, 10211–10224 (2022). https://doi.org/10.1007/s13369-021-06502-w

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