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Dilated Volumetric Network: an Enhanced Fully Convolutional Network for Volumetric Prostate Segmentation from Magnetic Resonance Imaging

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Abstract

Early detection of prostate cancer is crucial for its successful treatment. However, it is not always an easy task because of the various image capturing configurations, like acquisition protocols, magnetic field strength, presence/absence of endorectal coil, and resolution. The major bottleneck in the process is the delineation of the prostate boundary for its localization, which is required for the detection of abnormalities and performing radiotherapy accurately. Phenomenal development in Artificial Intelligence and Deep Learning has been contributing significantly to medical diagnostics using Computer Vision and the self-learning capabilities of Deep Learning has been explored to present a viable solution to automate this repetitive task of prostate segmentation. The previous approaches of 2D segmentation do not capture volumetric information and are very time consuming too. Hence, we have developed a Deep Learning based automated solution called DV-Net (Dilated Volumetric Network) for volumetric segmentation of prostate cancer. The proposed method considers the full prostate volume in 3D and requires minimal post-processing, which makes it less dependent on the type of input. We also focus on increasing the receptive field of the network and use deep supervision for better segmentation accuracy. Owing to all these features, DV-Net has shown to outperform the accuracy of the baseline V-Net model on the Prostate MR Image Segmentation (PROMISE) data set.

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Notes

  1. https://www.wcrf.org/dietandcancer/prostate-cancer

  2. https://promise12.grand-challenge.org

  3. https://www.slicer.org

  4. link to segmentation results in video http://bit.ly/37TjBKr

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Institute of Technology, Nirma University, 382481, Ahmedabad, Gujarat, India

    Aman Agarwal, Aditya Mishra, Madhushree Basavarajaiah, Priyanka Sharma & Sudeep Tanwar

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  1. Aman Agarwal
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  2. Aditya Mishra
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  3. Madhushree Basavarajaiah
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Corresponding authors

Correspondence to Aman Agarwal, Aditya Mishra, Madhushree Basavarajaiah, Priyanka Sharma or Sudeep Tanwar.

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The authors declare that they have no conflicts of interest. This article does not contain any studies involving animals or human participants performed by any of the authors.

Additional information

Aman Agarwal was born in 1997 and graduated with a degree in Computer Engineering from the Institute of Technology, Nirma University, Ahmedabad, Gujarat, India in 2019. During the undergraduate period, his research interests included the application of machine learning and deep learning techniques in medical imaging, satellite imaging, signal processing, parallel computing, and natural language processing. He has several years of programming experience. Currently, he is working as a software engineer in a software company in India.

Aditya Mishra earned his Bachelor’s degree in Computer Engineering in the year 2019 from the Institute of Technology, Nirma University, Ahmedabad, Gujarat, India. His research interests include Computer Vision, Audio Processing, Natural Language Processing, High-Performance Computing and their applications in the field of Medical Imaging, Nuclear Physics, Satellite Imaging, Agricultural Science, Speech Synthesis and Media and Entertainment. Currently, he is working as a Product Engineer in a startup based out of Bengaluru, India.

Madhushree Basavarajaiah is a Ph.D. Research Scholar working under the supervision of Dr. Priyanka Sharma at the Institute of Technology, Nirma University, Ahmedabad. Her research areas include Video processing, Machine Learning, and Deep Learning. She received a Bachelor’s degree in Information Science and Engineering in 2011 and a Master’s degree in Web Technology in 2013 from University Visvesvaraya College of Engineering, Bengaluru. She has teaching experience of three years at undergraduate and postgraduate levels. She is an active member of ACM and IEEE.

Dr. Priyanka Sharma has received her BE in Computer Engineering from L D Engineering College (1999), MTech in Computer Science and Engineering and PhD from Nirma University (2012), Gujarat, India. She is NVIDIA Certified Deep Learning University Ambassador and has over 20 years of teaching and industrial experience. Her international exposure includes visits to Silicon Valley, USA and Halifax and Toronto in Canada for research-based collaborations/presentations. She has completed three research projects funded by Shastri Indo-Canadian Research Grant, IPR and GUJCOST and is currently working on 2 projects funded by BRNS-DAE. She has also worked as the Principal Investigator of NVIDIA Research Center (2014). She has received various national level awards for her work. She is also involved as an Adviser/Member at various academic and research bodies at Nirma University and is also a member of various professional bodies like IEEE, ACM, IEEE Sensors Council, IEEE Biometrics Council, IEEE Signal Processing Society and Life Member of ISTE and CSI.

Dr. Sudeep Tanwar is an Associate Professor in the Computer Science and Engineering Department at the Institute of Technology, Nirma University, Ahmedabad, Gujarat, India. He is visiting Professor at Jan Wyzykowski University in Polkowice, Poland and the University of Pitesti in Pitesti, Romania. He received B.Tech in 2002 from Kurukshetra University, India, M.Tech (Honor’s) in 2009 from Guru Gobind Singh Indraprastha University, Delhi, India, and Ph.D. in 2016 with specialization in Wireless Sensor Network. He has authored or coauthored more than 130 technical research papers published in leading journals and conferences from the IEEE, Elsevier, Springer, Wiley, etc. He has also published six edited/authored books with International/National Publishers like IET, Springer. He has guided many students leading to M.E./M.Tech and guiding students leading to Ph.D. He is Associate Editor of IJCS, Wiley and Security and Privacy Journal, Wiley. His current interest includes Wireless Sensor Networks, Fog Computing, Smart Grid, IoT, and Blockchain Technology. He was invited as Guest Editors/Editorial Board Members of many International Journals, invited for keynote Speaker in many International Conferences held in Asia and invited as Program Chair, Publications Chair, Publicity Chair, and Session Chair in many International Conferences held in North America, Europe, Asia, and Africa. He has been awarded the best research paper awards from IEEE GLOBECOM 2018, IEEE ICC 2019, and Springer ICRIC-2019.

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Aman Agarwal, Mishra, A., Basavarajaiah, M. et al. Dilated Volumetric Network: an Enhanced Fully Convolutional Network for Volumetric Prostate Segmentation from Magnetic Resonance Imaging. Pattern Recognit. Image Anal. 31, 228–239 (2021). https://doi.org/10.1134/S1054661821020024

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