Lesion Detection by Efficiently Bridging 3D Context

Zhishuai Zhang, Yuyin Zhou, Wei Shen, Elliot Fishman, Alan Yuille

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

Abstract

Lesion detection in CT (computed tomography) scan images is an important yet challenging task due to the low contrast of soft tissues and similar appearance between lesion and the background. Exploiting 3D context information has been studied extensively to improve detection accuracy. However, previous methods either use a 3D CNN which usually requires a sliding window strategy to inference and only acts on local patches; or simply concatenate feature maps of independent 2D CNNs to obtain 3D context information, which is less effective to capture 3D knowledge. To address these issues, we design a hybrid detector to combine benefits from both of the above methods. We propose to build several light-weighted 3D CNNs as subnets to bridge 2D CNNs’ intermediate features, so that 2D CNNs are connected with each other which interchange 3D context information while feed-forwarding. Comprehensive experiments in DeepLesion dataset show that our method can combine 3D knowledge effectively and provide higher quality backbone features. Our detector surpasses the current state-of-the-art by a large margin with comparable speed and GPU memory consumption.

Original languageEnglish (US)
Title of host publicationMachine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsHeung-Il Suk, Mingxia Liu, Chunfeng Lian, Pingkun Yan
PublisherSpringer
Pages470-478
Number of pages9
ISBN (Print)9783030326913
DOIs
Publication statusPublished - Jan 1 2019
Event10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 13 2019Oct 13 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11861 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/13/1910/13/19

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhang, Z., Zhou, Y., Shen, W., Fishman, E., & Yuille, A. (2019). Lesion Detection by Efficiently Bridging 3D Context. In H-I. Suk, M. Liu, C. Lian, & P. Yan (Eds.), Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 470-478). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11861 LNCS). Springer. https://doi.org/10.1007/978-3-030-32692-0_54