Mr. Mahmoud K. Abdelhamed Profile

Mahmoud K. Abdelhamed

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 7 April 2023 Poster + Paper

NesT UNet: pure transformer segmentation network with an application for automatic cardiac myocardial infarction evaluation

Mahmoud Abdelhamed, Fabrice Meriaudeau

Proceedings Volume 12465, 124652L (2023) https://doi.org/10.1117/12.2654162

KEYWORDS: Medical imaging, Feature extraction, Convolution, 3D modeling, Heart, Tissues, Myocardium, Image segmentation, Transformers, Education and training

Read Abstract +

Myocardial Infarction (MI), commonly known as heart attack, is the irreversible death of the Myocardium’s tissue due to oxygen deprivation for an extended period of time. LGE-MRI scans are considered the defacto in the diagnosis and prognosis of MI. Still, they require manually segmenting the Myocardium and the infarcted tissue, which is a complex and time-consuming task. Hence, an automatic segmentation method of the Myocardium tissue is highly desirable. CNNs (Convolutional Neural Networks) are used extensively in cardiac tissue segmentation in general and for solving this problem particularly. EMIDEC (automatic Evaluation of Myocardial Infarction from Delayed Enhancement Cardiac MRI) challenge in MICCAI 2020 provides a good overview of the different CNN-based architectures used for MI segmentation. Still, they required complex pipelines to achieve state-of-the-art results and the research followed the challenge tends to go in the same direction of building more complex pipelines revolving around the famous CNN-UNet either 2D or 3D. In this paper, we present a different direction by presenting a simple 2D novel architecture based on Self-Attention Transformers offering a possible alternative to the CNN-UNet as a building block for bigger systems. We introduce NesT-UNet a novel segmentation architecture based on the NesT architecture as an encoder and we also introduce a novel decoder inspired by the same architecture. NesT achieved state-of-the-art results on ImageNet and CIFAR classification tasks with minimal training compared to other transformer networks. 2D NesT-UNet produced results comparable to the state-of-the-art on the EMIDEC dataset using a simple training process and an extra self-supervised pre-training step to improve the network’s performance. we also present a novel loss function for false positives reduction.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years