Automatic liver tumor segmentation in follow-up CT scans: Preliminary method and results

Refael Vivanti*, Ariel Ephrat, Leo Joskowicz, Naama Lev-Cohain, Onur A. Karaaslan, Jacob Sosna

*Corresponding author for this work

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

21 Scopus citations

Abstract

We present a new, fully automatic algorithm for liver tumors segmentation in follow-up CT studies. The inputs are a baseline CT scan and a delineation of the tumors in it and a follow-up scan; the outputs are the tumors delineations in the follow-up CT scan. The algorithm starts by defining a region of interest using a deformable registration of the baseline scan and tumors delineations to the follow-up CT scan and automatic liver segmentation. Then, it constructs a voxel classifier by training a Convolutional Neural Network (CNN). Finally, it segments the tumor in the follow-up study with the learned classifier. The main novelty of our method is the combination of follow-up based detection with CNN-based segmentation. Our experimental results on 67 tumors from 21 patients with ground-truth segmentations approved by a radiologist yield a success rate of 95.4% and an average overlap error of 16.3% (std = 10.3).

Original languageAmerican English
Title of host publicationPatch-Based Techniques in Medical Imaging - First st International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers
EditorsPierrick Coupé, Brent Munsell, Guorong Wu, Yiqiang Zhan, Daniel Rueckert
PublisherSpringer Verlag
Pages54-61
Number of pages8
ISBN (Print)9783319281933
DOIs
StatePublished - 2015
Event1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015 - Munich, Germany
Duration: 9 Oct 20159 Oct 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9467
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015
Country/TerritoryGermany
CityMunich
Period9/10/159/10/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

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