Sensors, Vol. 20, Pages 3183: Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI

Sensors, Vol. 20, Pages 3183: Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI Sensors doi: 10.3390/s20113183 Authors: Zia Khan Norashikin Yahya Khaled Alsaih Syed Saad Azhar Ali Fabrice Meriaudeau In this paper, we present an evaluation of four encoder–decoder CNNs in the segmentation of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected CNNs are FCN, SegNet, U-Net, and DeepLabV3+, which was originally proposed for the segmentation of road scene, biomedical, and natural images. Segmentation of prostate in T2W MRI images is an important step in the automatic diagnosis of prostate cancer to enable better lesion detection and staging of prostate cancer. Therefore, many research efforts have been conducted to improve the segmentation of the prostate gland in MRI images. The main challenges of prostate gland segmentation are blurry prostate boundary and variability in prostate anatomical structure. In this work, we investigated the performance of encoder–decoder CNNs for segmentation of prostate gland in T2W MRI. Image pre-processing techniques including image resizing, center-cropping and intensity normalization are applied to address the issues of inter-patient and inter-scanner variability as well as the issue of dominating background pixels over prostate pixels. In addition, to enrich the network with more data, to increase data variation, and to improve its accurac...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research