Skip to main content
SpringerLink
Log in

Automated Machine Learning Algorithm for Kidney, Kidney Tumor, Kidney Cyst Segmentation in Computed Tomography Scans

  • Conference paper
  • First Online:
Kidney and Kidney Tumor Segmentation (KiTS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13168))

Included in the following conference series:

  • 658 Accesses

Abstract

In this paper, we have described an automated algorithm for accurate segmentation of kidney, kidney tumors, and kidney cysts from CT scans. The Dataset for this problem was made available online as part of KiTS21 Challenge. Our approach was placed 13th in the official leaderboard of the competition. Our model uses a 2 stage Residual Unet architecture. The first network is designed to predict (Kidney + Tumor + Cyst) regions. The second network predicts segmented tumor and cyst regions from the output of the first network. The paper contains implementation details along with results on the official test and internal set.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kidney Cancer Statistics. wcrf.org/dietandcancer/cancer-trends/kidney-cancer-statistics

  2. Cancer Diagnosis and Treatment Statistics. Stages — Mesothelioma — CancerResearch UK, 26 October 2017

    Google Scholar 

  3. KiTS21 Challenge. https://kits21.kits-challenge.org/

  4. Nikolov, S., et al.: Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy. arXiv preprint arXiv:1809.04430 (2018)

Download references

Author information

Authors and Affiliations

  1. Endimension Technology Private Limited (Incubator Under SINE IIT Mumbai), Mumbai, India

    Vivek Pawar & Bharadwaj Kss

Authors
  1. Vivek Pawar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bharadwaj Kss
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vivek Pawar .

Editor information

Editors and Affiliations

  1. University of Minnesota, Minneapolis, MN, USA

    Nicholas Heller

  2. German Cancer Research Center (DKFZ), Heidelberg, Germany

    Fabian Isensee

  3. German Cancer Research Center (DKFZ), Heidelberg, Germany

    Darya Trofimova

  4. University of Minnesota, Minneapolis, MN, USA

    Resha Tejpaul

  5. University of Minnesota, Minneapolis, MN, USA

    Nikolaos Papanikolopoulos

  6. Cleveland Clinic, Cleveland, OH, USA

    Christopher Weight

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pawar, V., Kss, B. (2022). Automated Machine Learning Algorithm for Kidney, Kidney Tumor, Kidney Cyst Segmentation in Computed Tomography Scans. In: Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Papanikolopoulos, N., Weight, C. (eds) Kidney and Kidney Tumor Segmentation. KiTS 2021. Lecture Notes in Computer Science, vol 13168. Springer, Cham. https://doi.org/10.1007/978-3-030-98385-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-98385-7_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98384-0

  • Online ISBN: 978-3-030-98385-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation