Robust Segmentation of the Full Cerebral Vasculature in 4D CT of Suspected Stroke Patients.

Abstract:

:A robust method is presented for the segmentation of the full cerebral vasculature in 4-dimensional (4D) computed tomography (CT). The method consists of candidate vessel selection, feature extraction, random forest classification and postprocessing. Image features include among others the weighted temporal variance image and parameters, including entropy, of an intensity histogram in a local region at different scales. These histogram parameters revealed to be a strong feature in the detection of vessels regardless of shape and size. The method was trained and tested on a large database of 264 patients with suspicion of acute ischemia who underwent 4D CT in our hospital in the period January 2014 to December 2015. Five subvolumes representing different regions of the cerebral vasculature were annotated in each image in the training set by medical assistants. The evaluation was done on 242 patients. A total of 16 (<8%) patients showed severe under or over segmentation and were reported as failures. One out of five subvolumes was randomly annotated in 159 patients and was used for quantitative evaluation. Quantitative evaluation showed a Dice coefficient of 0.91 ± 0.07 and a modified Hausdorff distance of 0.23 ± 0.22 mm. Therefore, robust vessel segmentation in 4D CT is feasible with good accuracy.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Meijs M,Patel A,van de Leemput SC,Prokop M,van Dijk EJ,de Leeuw FE,Meijer FJA,van Ginneken B,Manniesing R

doi

10.1038/s41598-017-15617-w

subject

Has Abstract

pub_date

2017-11-15 00:00:00

pages

15622

issue

1

issn

2045-2322

pii

10.1038/s41598-017-15617-w

journal_volume

7

pub_type

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