Prostate Gland Segmentation using Semantic Segmentation Models U-Net and LinkNet

Prostate Gland Segmentation using Semantic Segmentation Models U-Net and LinkNet

© 2022 by IJETT Journal
Volume-70 Issue-12
Year of Publication : 2022
Author : M. N. Rajesh, B. S. Chandrasekar
DOI : 10.14445/22315381/IJETT-V70I12P224

How to Cite?

M. N. Rajesh, B. S. Chandrasekar, "Prostate Gland Segmentation using Semantic Segmentation Models U-Net and LinkNet," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 252-271, 2022. Crossref,

The segmentation and classification of the prostate lesion or malignant growth through manual observation are highly challenging. Machine learning-based semantic segmentation architecture was used to segment the diseases based on lesion appearance and characteristics automatically. Still, those models consume more energy and processing time and will lead to reduced scalability and reliability. To tackle these limitations, deep learning (DL) based semantic segmentation architecture can be implemented, which has more advantages in discriminating the features of the lesions efficiently and accurately. This paper proposes DL-based semantic segmentation models such as LinkNet, U-Net, PSPNet and FPN. These proposed segmentation models are integrated with convolutional neural network (CNN) based backbone architectures like ResNet-34 and SE-ResNet-34. Initially, the nearest neighbour interpolation technique is employed as preprocessing technique for scaling the image. Next, normalization of intensity is employed to minimize the variations in the intensity distributions of the image. Normalized image is used for processing various settings of LinkNet and U-Net architectures. Furthermore, the proposed model uses hyper-parameter optimization with optimizers such as Adam, Adamax, Stochastic Gradient Descent, RMSProp, and Nadam for both U-Net and LinkNet to minimize the complexity of the network and enhance computing efficiency. Experimental results have been evaluated using Python on Google Colab with NCI-ISBI 2013 dataset. Performance analysis of the proposed model is assessed in terms of the Intersection of the Union (IoU) score. The LinkNet with SE-ResNet-34 model optimized using Adamax generated the best result with 0.7454453 IoU score, following U-Net with SE-ResNet-34 optimized using Adamax generated 0.738271933 IoU.

Deep learning, FPN, Prostate cancer, PSP-Net, LinkNet, ResNet-34, Semantic segmentation, SE-ResNet-34, U-Net.

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