Learning 3D Features with 2D CNNs via Surface Projection for CT Volume Segmentation

Youyi Song, Zhen Yu, Teng Zhou, Jeremy Yuen Chun Teoh, Baiying Lei, Kup Sze Choi, Jing Qin

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)


3D features are desired in nature for segmenting CT volumes. It is, however, computationally expensive to employ a 3D convolutional neural network (CNN) to learn 3D features. Existing methods hence learn 3D features by still relying on 2D CNNs while attempting to consider more 2D slices, but up until now it is difficulty for them to consider the whole volumetric data, resulting in information loss and performance degradation. In this paper, we propose a simple and effective technique that allows a 2D CNN to learn 3D features for segmenting CT volumes. Our key insight is that all boundary voxels of a 3D object form a surface that can be represented by using a 2D matrix, and therefore they can be perfectly recognized by a 2D CNN in theory. We hence learn 3D features for recognizing these boundary voxels by learning the projection distance between a set of prescribed spherical surfaces and the object’s surface, which can be readily performed by a 2D CNN. By doing so, we can consider the whole volumetric data when spherical surfaces are sampled sufficiently dense, without any information loss. We assessed the proposed method on a publicly available dataset. The experimental evidence shows that the proposed method is effective, outperforming existing methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030597184
Publication statusPublished - Sep 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 4 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12264 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020


  • CT image segmentation
  • Learning 3D features by 2D CNNs
  • Surface projection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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