Fine-grained tumor segmentation on computed tomography slices by leveraging bottom-up and top-down strategies

Zhenmei Yu, Shuchao Pang*, Anan Du, Mehmet Orgun, Yan Wang, Hong Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

Fully Convolutional Neural Networks (FCNNs) have been widely employed to solve object segmentation tasks effectively in both the computer vision and medical image processing fields in recent years. In object segmentation, FCNNs play a pixel-level prediction role in generating segmented predictions pixel-by-pixel, but they ignore the relationships among generated pixels on the output image. Moreover, blurry boundaries of predicted objects are another common obstacle in this task, because FCNNs usually generate low-frequency components of an image well, but they lack clear high-frequency information inside. In order to solve these problems, we introduce a top-down strategy by globally considering object shapes and context information. Moreover, based on original pixel-wise loss functions which we call a bottom-up strategy, we formulate the tumor segmentation task as a regression problem by using Jaccard Similarity Coefficient (JSC) which is usually one of the main metrics for evaluating the performance of segmentation methods. We directly propose a JSC loss to further optimize network parameters for globally evaluating the whole outputs of tumors. Furthermore, the new loss also alleviates the effect of severe class imbalanced problems between tumor regions and non-tumor regions when training FCNNs. By leveraging the bottom-up and top-down strategies together, our model can obtain more fine-grained tumor segmentation results and also be easily embedded into any FCNN framework for other object segmentation tasks. Detailed experimental results on common metrics demonstrate the superior performance of our proposed method for Kidney Tumor Segmentation Challenge 2019 among more than 100 involved teams.
Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage Processing
EditorsIvana Išgum, Bennett A. Landman
Place of PublicationBellingham, WA
PublisherSPIE
Pages1-7
Number of pages7
ISBN (Electronic)9781510633940
ISBN (Print)9781510633933
DOIs
Publication statusPublished - 2020
EventMedical Imaging 2020: Image Processing - Houston, United States
Duration: 17 Feb 202020 Feb 2020

Conference

ConferenceMedical Imaging 2020
CountryUnited States
CityHouston
Period17/02/2020/02/20

Keywords

  • Kidney tumor segmentation
  • computed tomography
  • fully convolutional neural networks
  • structured learning
  • Computed tomography
  • Fully convolutional neural networks
  • Structured learning

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