TPCNN: Two-phase patch-based convolutional neural network for automatic brain tumor segmentation and survival prediction

Fan Zhou, Tengfei Li, Heng Li, Hongtu Zhu

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

Abstract

The aim of this paper is to integrate some advanced statistical methods with modern deep learning methods for tumor segmentation and survival time prediction in the BraTS 2017 challenge. The goals of the BraTS 2017 challenge are to utilize multi-institutional pre-operative MRI scans to segment out different tumor subregions and then to use tumor information to predict patient’s overall survival. We build a two-phase patch-based convolutional neural network (TPCNN) model to classify all the pixels in the brain and further refine the segmentation results by using XGBoost and a post-processing procedure. The segmentation results are then used to extract various informative radiomic features for prediction of the survival time by using the XGBoost method.

Original languageEnglish (US)
Title of host publicationBrainlesion
Subtitle of host publicationGlioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 3rd International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Revised Selected Papers
EditorsBjoern Menze, Alessandro Crimi, Hugo Kuijf, Mauricio Reyes, Spyridon Bakas
PublisherSpringer Verlag
Pages274-286
Number of pages13
ISBN (Print)9783319752372
DOIs
Publication statusPublished - Jan 1 2018
Externally publishedYes
Event3rd International Workshop on Brainlesion, BrainLes 2017 Held in Conjunction with Medical Image Computing for Computer Assisted Intervention , MICCAI 2017 - Quebec City, Canada
Duration: Sep 14 2017Sep 14 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10670 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Workshop on Brainlesion, BrainLes 2017 Held in Conjunction with Medical Image Computing for Computer Assisted Intervention , MICCAI 2017
CountryCanada
CityQuebec City
Period9/14/179/14/17

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Keywords

  • Convolutional neural network
  • Patch-based
  • XGBoost

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhou, F., Li, T., Li, H., & Zhu, H. (2018). TPCNN: Two-phase patch-based convolutional neural network for automatic brain tumor segmentation and survival prediction. In B. Menze, A. Crimi, H. Kuijf, M. Reyes, & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 3rd International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Revised Selected Papers (pp. 274-286). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10670 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-75238-9_24