Prostate Segmentation via Dynamic Fusion Model

• Published in: Arabian journal for science and engineering (2011) 2022-08, Vol.47 (8), p.10211-10224
• Main Authors: Ocal, Hakan, Barisci, Necaattin
• Format: Article
• Language: English
• Subjects: Datasets; Engineering; Humanities and Social Sciences; Image segmentation; Medical imaging; multidisciplinary; Research Article-Computer Engineering and Computer Science; Science
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-021-06502-w

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.