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Evaluation of deep neural networks for semantic segmentation of prostate in T2W MRI

Khan, Z. and Yahya, N. and Alsaih, K. and Ali, S.S.A. and Meriaudeau, F. (2020) Evaluation of deep neural networks for semantic segmentation of prostate in T2W MRI. Sensors (Switzerland), 20 (11). pp. 1-17.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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 accuracy, patch extraction and data augmentation are applied prior to training the networks. Furthermore, class weight balancing is used to avoid having biased networks since the number of background pixels is much higher than the prostate pixels. The class imbalance problem is solved by utilizing weighted cross-entropy loss function during the training of the CNN model. The performance of the CNNs is evaluated in terms of the Dice similarity coefficient (DSC) and our experimental results show that patch-wise DeepLabV3+ gives the best performance with DSC equal to 92.8. This value is the highest DSC score compared to the FCN, SegNet, and U-Net that also competed the recently published state-of-the-art method of prostate segmentation. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Item Type:Article
Impact Factor:cited By 6
Uncontrolled Keywords:Decoding; Deep neural networks; Diseases; Image enhancement; Magnetic resonance imaging; Pixels; Semantic Web; Semantics; Signal encoding; Urology, Anatomical structures; Automatic diagnosis; Class imbalance problems; Image preprocessing; Prostate segmentation; Semantic segmentation; Similarity coefficients; State-of-the-art methods, Image segmentation, diagnostic imaging; human; image processing; male; nuclear magnetic resonance imaging; prostate; semantics, Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Neural Networks, Computer; Prostate; Semantics
ID Code:23236
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 06:08
Last Modified:19 Aug 2021 06:08

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