S4ND: Single-shot single-scale lung nodule detection

Naji Khosravan*, Ulas Bagci

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

37 Scopus citations

Abstract

The most recent lung nodule detection studies rely on computationally expensive multi-stage frameworks to detect nodules from CT scans. To address this computational challenge and provide better performance, in this paper we propose S4ND, a new deep learning based method for lung nodule detection. Our approach uses a single feed forward pass of a single network for detection. The whole detection pipeline is designed as a single 3D Convolutional Neural Network (CNN) with dense connections, trained in an end-to-end manner. S4ND does not require any further post-processing or user guidance to refine detection results. Experimentally, we compared our network with the current state-of-the-art object detection network (SSD) in computer vision as well as the state-of-the-art published method for lung nodule detection (3D DCNN). We used publicly available 888 CT scans from LUNA challenge dataset and showed that the proposed method outperforms the current literature both in terms of efficiency and accuracy by achieving an average FROC-score of 0.897. We also provide an in-depth analysis of our proposed network to shed light on the unclear paradigms of tiny object detection.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
EditorsGabor Fichtinger, Christos Davatzikos, Carlos Alberola-López, Alejandro F. Frangi, Julia A. Schnabel
PublisherSpringer Verlag
Pages794-802
Number of pages9
ISBN (Print)9783030009335
DOIs
StatePublished - 2018
Externally publishedYes
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: Sep 16 2018Sep 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11071 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/TerritorySpain
CityGranada
Period9/16/189/20/18

Keywords

  • Deep learning
  • Dense CNN
  • Lung nodule detection
  • Object detection
  • Tiny object detection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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