Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks

Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks

Citation Author(s):
Anup
Tuladhar
University of Calgary
Serena
Schimert
University of Calgary
Deepthi
Rajashekar
University of Calgary
Helge
Kniep
University Medical Center Hamburg-Eppendorf
Jens
Fiehler
University Medical Center Hamburg-Eppendorf
Nils
Forkert
University of Calgary
Submitted by:
Anup Tuladhar
Last updated:
Thu, 05/28/2020 - 18:17
DOI:
10.21227/jps9-0b57
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Non-contrast computed tomography (NCCT) is commonly used for volumetric follow-up assessment of ischemic strokes. However, manual lesion segmentation is time-consuming and subject to high inter-observer variability. The aim of this study was to develop and establish a baseline convolutional neural network (CNN) model for automatic NCCT lesion segmentation. A total of 252 multi center clinical NCCT datasets, acquired from 22 centers, and corresponding manual segmentations were used to train  (204 datasets) and validate (48 datasets) a 3D multi scale CNN model for lesion segmentation. Post processing methods were implemented to improve the CNN-based lesion segmentations. The final CNN model and post processing method was evaluated using 39 out of distribution holdout test datasets, acquired at seven centers that did not contribute to the training or validation datasets. Each test image was segmented by two or three neuroradiologists. The Dice similarity coefficient (DSC) and predicted lesion volumes were used to evaluate the segmentations. The CNN model achieved a mean DSC score of 0.47 on the validation NCCT datasets. Post-processing significantly improved the DSC to 0.50 (P<0.01). On the holdout test set, the CNN model achieved a mean DSC score of 0.42, which was also significantly improved to 0.45 (P<0.05) by post processing. Importantly, the automatically segmented lesion volumes were not significantly different from the lesion volumes determined by the expert observers (P>0.05) and showed excellent agreement with manual lesion segmentation volumes (intraclass correlation coefficient, ICC = 0.88). The proposed CNN model can automatically and reliably segment ischemic stroke lesions in clinical NCCT datasets. Post processing techniques can further improve accuracy. As the model was trained and evaluated on datasets from multiple centers, it is broadly applicable and is publicly available.

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[1] Anup Tuladhar, Serena Schimert, Deepthi Rajashekar, Helge Kniep, Jens Fiehler, Nils Forkert, "Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks", IEEE Dataport, 2020. [Online]. Available: http://dx.doi.org/10.21227/jps9-0b57. Accessed: Jun. 02, 2020.
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doi = {10.21227/jps9-0b57},
url = {http://dx.doi.org/10.21227/jps9-0b57},
author = {Anup Tuladhar; Serena Schimert; Deepthi Rajashekar; Helge Kniep; Jens Fiehler; Nils Forkert },
publisher = {IEEE Dataport},
title = {Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks},
year = {2020} }
TY - DATA
T1 - Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks
AU - Anup Tuladhar; Serena Schimert; Deepthi Rajashekar; Helge Kniep; Jens Fiehler; Nils Forkert
PY - 2020
PB - IEEE Dataport
UR - 10.21227/jps9-0b57
ER -
Anup Tuladhar, Serena Schimert, Deepthi Rajashekar, Helge Kniep, Jens Fiehler, Nils Forkert. (2020). Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks. IEEE Dataport. http://dx.doi.org/10.21227/jps9-0b57
Anup Tuladhar, Serena Schimert, Deepthi Rajashekar, Helge Kniep, Jens Fiehler, Nils Forkert, 2020. Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks. Available at: http://dx.doi.org/10.21227/jps9-0b57.
Anup Tuladhar, Serena Schimert, Deepthi Rajashekar, Helge Kniep, Jens Fiehler, Nils Forkert. (2020). "Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks." Web.
1. Anup Tuladhar, Serena Schimert, Deepthi Rajashekar, Helge Kniep, Jens Fiehler, Nils Forkert. Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks [Internet]. IEEE Dataport; 2020. Available from : http://dx.doi.org/10.21227/jps9-0b57
Anup Tuladhar, Serena Schimert, Deepthi Rajashekar, Helge Kniep, Jens Fiehler, Nils Forkert. "Automatic Segmentation of Stroke Lesions in Non-contrast Computed Tomography Datasets with Convolutional Neural Networks." doi: 10.21227/jps9-0b57