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Liver Lesion Detection from Weakly-Labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector

  • Sang-gil Lee
  • Jae Seok Bae
  • Hyunjae Kim
  • Jung Hoon Kim
  • Sungroh Yoon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

We present a focal liver lesion detection model leveraged by custom-designed multi-phase computed tomography (CT) volumes, which reflects real-world clinical lesion detection practice using a Single Shot MultiBox Detector (SSD). We show that grouped convolutions effectively harness richer information of the multi-phase data for the object detection model, while a naive application of SSD suffers from a generalization gap. We trained and evaluated the modified SSD model and recently proposed variants with our CT dataset of 64 subjects by five-fold cross validation. Our model achieved a 53.3% average precision score and ran in under three seconds per volume, outperforming the original model and state-of-the-art variants. Results show that the one-stage object detection model is a practical solution, which runs in near real-time and can learn an unbiased feature representation from a large-volume real-world detection dataset, which requires less tedious and time consuming construction of the weak phase-level bounding box labels.

Keywords

Deep learning Liver lesions Detection Multi-phase CT 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) [2018R1A2B3001628], the Interdisciplinary Research Initiatives Program from College of Engineering and College of Medicine, Seoul National University (800-20170166), Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1601-05, the Creative Industrial Technology Development Program [No. 10053249] funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea), and the Brain Korea 21 Plus Project in 2018.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sang-gil Lee
    • 1
  • Jae Seok Bae
    • 2
    • 3
  • Hyunjae Kim
    • 1
  • Jung Hoon Kim
    • 2
    • 3
    • 4
  • Sungroh Yoon
    • 1
  1. 1.Electrical and Computer EngineeringSeoul National UniversitySeoulKorea
  2. 2.RadiologySeoul National University HospitalSeoulKorea
  3. 3.RadiologySeoul National University College of MedicineSeoulKorea
  4. 4.Institute of Radiation MedicineSeoul National University Medical Research CenterSeoulKorea

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