@inproceedings{153df3cc9b4b491f857aecb29c1fe3d3,
title = "Automatic liver tumor segmentation in follow-up CT scans: Preliminary method and results",
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).",
author = "Refael Vivanti and Ariel Ephrat and Leo Joskowicz and Naama Lev-Cohain and Karaaslan, {Onur A.} and Jacob Sosna",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015 ; Conference date: 09-10-2015 Through 09-10-2015",
year = "2015",
doi = "https://doi.org/10.1007/978-3-319-28194-0_7",
language = "English",
isbn = "9783319281933",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "54--61",
editor = "Pierrick Coup{\'e} and Brent Munsell and Guorong Wu and Yiqiang Zhan and Daniel Rueckert",
booktitle = "Patch-Based Techniques in Medical Imaging - First st International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers",
address = "Germany",
}