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Unet based Xception Model for Prostate Cancer Segmentation from MRI Images

  • 1218: Engineering Tools and Applications in Medical Imaging
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Abstract

One of the most prevalent forms of tumor found in males all over the world is prostate cancer. The main risk factors are age and family history. Magnetic Resonance Imaging (MRI) is highly recommended for detecting and localizing prostate cancer. It is very important for precise segmentation of the prostate region in MRI scans to improve the treatment possibilities and the chance of patient survival with prostate cancer. Manually segmenting the prostate region is a daunting task and often time-consuming because of the variation in shapes of prostates among patients, poor delineation of the boundary, and the use of different MRI modes. In this paper, we propose an automatic segmentation model for the prostate regions in MRI scans based on Unet and Xception net. To boost the performance of model, local residual connections are added in the decoder stage of the Unet. The empirical results are compared to different Unet based models with different preprocessing methods to assess the effectiveness of the proposed model. The experimentations are performed to support the fact that the proposed model performs better than other methods taken under study.

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Acknowledgment

The datasets used in this work formed part of the task of promise12.

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Author notes

  1. Ekam Singh Chahal and Aarya Patel contributed equally to this work. Archana Purwar is a corresponding author.

Authors and Affiliations

  1. Department of Computer Science and Engineering/ Information Technology, Jaypee Institute of Information Technology, Noida, India

    Ekam Singh Chahal, Aarya Patel, Ayush Gupta, Archana Purwar & Dhanalekshmi G

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  1. Ekam Singh Chahal
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  2. Aarya Patel
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  3. Ayush Gupta
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  5. Dhanalekshmi G
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Correspondence to Archana Purwar.

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Chahal, E.S., Patel, A., Gupta, A. et al. Unet based Xception Model for Prostate Cancer Segmentation from MRI Images. Multimed Tools Appl 81, 37333–37349 (2022). https://doi.org/10.1007/s11042-021-11334-9

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  • DOI: https://doi.org/10.1007/s11042-021-11334-9

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