Fast and accurate lung tumor spotting and segmentation for boundary delineation on CT slices in a coarse-to-fine framework

Shuchao Pang, Anan Du, Xiaoli He, Jorge Diez, Mehmet A. Orgun

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

Abstract

Label noise and class imbalance are two of the critical challenges when training image-based deep neural networks, especially in the biomedical image processing domain. Our work focuses on how to address the two challenges effectively and accurately in the task of lesion segmentation from biomedical/medical images. To address the pixel-level label noise problem, we propose an advanced transfer training and learning approach with a detailed DICOM pre-processing method. To address the tumor/non-tumor class imbalance problem, we exploit a self-adaptive fully convolutional neural network with an automated weight distribution mechanism to spot the Radiomics lung tumor regions accurately. Furthermore, an improved conditional random field method is employed to obtain sophisticated lung tumor contour delineation and segmentation. Finally, our approach has been evaluated using several well-known evaluation metrics on the Lung Tumor segmentation dataset used in the 2018 IEEE VIP-CUP Challenge. Experimental results show that our weakly supervised learning algorithm outperforms other deep models and state-of-the-art approaches.

Original languageEnglish
Title of host publicationNeural information processing
Subtitle of host publication26th International Conference, ICONIP 2019 Sydney, NSW, Australia, December 12–15, 2019 Proceedings, Part IV
EditorsTom Gedeon, Kok Wai Wong, Minho Lee
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages589-597
Number of pages9
ISBN (Electronic)9783030368081
ISBN (Print)9783030368074
DOIs
Publication statusPublished - 2019
Event26th International Conference on Neural Information Processing, ICONIP 2019 - Sydney, Australia
Duration: 12 Dec 201915 Dec 2019

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer Nature Switzerland
Number1142
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference26th International Conference on Neural Information Processing, ICONIP 2019
CountryAustralia
CitySydney
Period12/12/1915/12/19

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Keywords

  • Boundary delineation
  • Lung tumor segmentation
  • Fully convolutional neural networks

Cite this

Pang, S., Du, A., He, X., Diez, J., & Orgun, M. A. (2019). Fast and accurate lung tumor spotting and segmentation for boundary delineation on CT slices in a coarse-to-fine framework. In T. Gedeon, K. W. Wong, & M. Lee (Eds.), Neural information processing: 26th International Conference, ICONIP 2019 Sydney, NSW, Australia, December 12–15, 2019 Proceedings, Part IV (pp. 589-597). (Communications in Computer and Information Science; No. 1142). Cham, Switzerland: Springer, Springer Nature. https://doi.org/10.1007/978-3-030-36808-1_64