dc.contributor.author |
Ahmad, Parvez |
|
dc.contributor.author |
Qamar, Saqib |
|
dc.contributor.author |
Shen, Linlin |
|
dc.contributor.author |
Saeed, Adnan |
|
dc.contributor.editor |
Bakas, S |
|
dc.coverage.spatial |
ELECTR NETWORK |
|
dc.date.accessioned |
2024-06-05T00:12:41Z |
|
dc.date.available |
2024-06-05T00:12:41Z |
|
dc.date.issued |
2021 |
|
dc.identifier.citation |
(2021). Lecture Notes in Computer Science, 12658, 207-218. |
|
dc.identifier.isbn |
9783030720834 |
|
dc.identifier.issn |
1611-3349 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/68554 |
|
dc.description.abstract |
Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contextual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose a modified UNet architecture for brain tumor segmentation. In the proposed architecture, we used densely connected blocks in both encoder and decoder paths to extract multi-contextual information from the concept of feature reusability. In addition, residual-inception blocks (RIB) are used to extract the local and global information by merging features of different kernel sizes. We validate the proposed architecture on the multi-modal brain tumor segmentation challenge (BRATS) 2020 testing dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) are 89.12%, 84.74%, and 79.12%, respectively. |
|
dc.publisher |
Springer Nature |
|
dc.relation.ispartof |
6th International MICCAI Brain-Lesion Workshop (BrainLes) |
|
dc.relation.ispartofseries |
BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES (BRAINLES 2020), PT I |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.subject |
4605 Data Management and Data Science |
|
dc.subject |
46 Information and Computing Sciences |
|
dc.subject |
Bioengineering |
|
dc.subject |
Brain Disorders |
|
dc.subject |
Cancer |
|
dc.subject |
Neurosciences |
|
dc.subject |
Science & Technology |
|
dc.subject |
Technology |
|
dc.subject |
Life Sciences & Biomedicine |
|
dc.subject |
Computer Science, Artificial Intelligence |
|
dc.subject |
Computer Science, Interdisciplinary Applications |
|
dc.subject |
Radiology, Nuclear Medicine & Medical Imaging |
|
dc.subject |
Computer Science |
|
dc.subject |
CNN |
|
dc.subject |
UNet |
|
dc.subject |
Contexual information |
|
dc.subject |
Dense connections |
|
dc.subject |
Residual inception blocks |
|
dc.subject |
Brain tumor segmentation |
|
dc.title |
Context Aware 3D UNet for Brain Tumor Segmentation |
|
dc.type |
Conference Item |
|
dc.identifier.doi |
10.1007/978-3-030-72084-1_19 |
|
pubs.begin-page |
207 |
|
pubs.volume |
12658 |
|
dc.date.updated |
2024-05-31T06:35:50Z |
|
dc.rights.holder |
Copyright: Springer Nature Switzerland AG |
en |
pubs.end-page |
218 |
|
pubs.publication-status |
Published |
|
pubs.start-date |
2020-10-04 |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RetrictedAccess |
en |
pubs.elements-id |
1029143 |
|
pubs.org-id |
Bioengineering Institute |
|
dc.identifier.eissn |
1611-3349 |
|
pubs.record-created-at-source-date |
2024-05-31 |
|
pubs.online-publication-date |
2021-03-27 |
|