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Research article
Weilin Huang
Corresponding Author
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MR images (MRIs) accurate segmentation of brain lesions is important for improving cancer diagnosis, surgical planning, and prediction of outcome. However, manual and accurate segmentation of brain lesions from 3D MRIs is highly expensive, time-consuming, and prone to user biases. We present an efficient yet conceptually simple brain segmentation network (referred as Brain SegNet), which is a 3D residual framework for automatic voxel-wise segmentation of brain lesion. Our model is able to directly predict dense voxel segmentation of brain tumor or ischemic stroke regions in 3D brain MRIs. The proposed 3D segmentation network can run at about 0.5s per MRIs - about 50 times faster than previous approaches 1,2. Our model is evaluated on the BRATS 2015 benchmark for brain tumor segmentation, where it obtains state-of-the-art results, by surpassing recently published results reported in 1,2. We further applied the proposed Brain SegNet for ischemic stroke lesion outcome prediction, with impressive results achieved on the Ischemic Stroke Lesion Segmentation (ISLES) 2017 database.
Keywords
Brain tumor segmentation, stroke outcome prediction, 3D brain MRIs, curriculum learning
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CURRENT STATUS: Published
View the published article at BMC Medical Imaging.
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Posted 07 Jan, 2020
No community comments so far
Submission checks complete
On 04 Jan, 2020
Editorial decision: Accept
On 03 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 26 Dec, 2019
Editor assigned
On 08 Dec, 2019
Editor invited
On 07 Dec, 2019
Submission checks complete
On 02 Dec, 2019
No comments provided
Editorial decision: Minor revision
On 22 Nov, 2019
Review #2 received
Received 19 Nov, 2019
Review #1 received
Received 05 Nov, 2019
Reviewer #2 agreed
On 30 Oct, 2019
Reviewer #1 agreed
On 15 Oct, 2019
Reviewers invited
Invitations sent on 26 Sep, 2019
Submission checks complete
On 17 Sep, 2019
Editor assigned
On 16 Sep, 2019
Editor invited
On 15 Sep, 2019
First submitted
On 12 Sep, 2019
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Subject Areas
Nuclear Medicine & Medical Imaging
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A new preprint design is coming!
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See the published version of this preprint at BMC Medical Imaging.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Weilin Huang
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Imaging.
Version 3
Posted 07 Jan, 2020
No community comments so far
Submission checks complete
On 04 Jan, 2020
Editorial decision: Accept
On 03 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 26 Dec, 2019
Editor assigned
On 08 Dec, 2019
Editor invited
On 07 Dec, 2019
Submission checks complete
On 02 Dec, 2019
No comments provided
Editorial decision: Minor revision
On 22 Nov, 2019
Review #2 received
Received 19 Nov, 2019
Review #1 received
Received 05 Nov, 2019
Reviewer #2 agreed
On 30 Oct, 2019
Reviewer #1 agreed
On 15 Oct, 2019
Reviewers invited
Invitations sent on 26 Sep, 2019
Submission checks complete
On 17 Sep, 2019
Editor assigned
On 16 Sep, 2019
Editor invited
On 15 Sep, 2019
First submitted
On 12 Sep, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Nuclear Medicine & Medical Imaging
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Imaging.
MR images (MRIs) accurate segmentation of brain lesions is important for improving cancer diagnosis, surgical planning, and prediction of outcome. However, manual and accurate segmentation of brain lesions from 3D MRIs is highly expensive, time-consuming, and prone to user biases. We present an efficient yet conceptually simple brain segmentation network (referred as Brain SegNet), which is a 3D residual framework for automatic voxel-wise segmentation of brain lesion. Our model is able to directly predict dense voxel segmentation of brain tumor or ischemic stroke regions in 3D brain MRIs. The proposed 3D segmentation network can run at about 0.5s per MRIs - about 50 times faster than previous approaches 1,2. Our model is evaluated on the BRATS 2015 benchmark for brain tumor segmentation, where it obtains state-of-the-art results, by surpassing recently published results reported in 1,2. We further applied the proposed Brain SegNet for ischemic stroke lesion outcome prediction, with impressive results achieved on the Ischemic Stroke Lesion Segmentation (ISLES) 2017 database.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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