no code implementations • 13 Jul 2024 • Md Rakibul Islam, Riad Hassan, Abdullah Nazib, Kien Nguyen, Clinton Fookes, Md Zahidul Islam
Deep learning has achieved outstanding accuracy in medical image segmentation, particularly for objects like organs or tumors with smooth boundaries or large sizes.
1 code implementation • 19 Mar 2023 • Abdullah Nazib, Riad Hassan, Zahidul Islam, Clinton Fookes
For accurate segmentation, we also proposed a CT intensity integrated regularization loss.
no code implementations • 15 Feb 2020 • Abdullah Nazib, Clinton Fookes, Olivier Salvado, Dimitri Perrin
The recent application of deep learning technologies in medical image registration has exponentially decreased the registration time and gradually increased registration accuracy when compared to their traditional counterparts.
no code implementations • 13 Jun 2019 • Abdullah Nazib, Clinton Fookes, Dimitri Perrin
In both resolutions, the proposed DenseDeformation network outperforms VoxelMorph in registration accuracy.
no code implementations • 19 Oct 2018 • Abdullah Nazib, Clinton Fookes, Dimitri Perrin
In this paper, we investigate and compare the performance of a deep learning based registration method with traditional optimization based methods on samples from tissue-clearing methods.
no code implementations • 13 Jul 2018 • Abdullah Nazib, James Galloway, Clinton Fookes, Dimitri Perrin
Recent progress in tissue clearing has allowed for the imaging of entire organs at single-cell resolution.