Search Results for author: Michelle Noga

Found 6 papers, 0 papers with code

Unsupervised diffeomorphic cardiac image registration using parameterization of the deformation field

no code implementations28 Aug 2022 Ameneh Sheikhjafari, Deepa Krishnaswamy, Michelle Noga, Nilanjan Ray, Kumaradevan Punithakumar

Finally, it is suitable for cardiac data processing, since the nature of this parameterization is to define the deformation field in terms of the radial and rotational components.

Image Registration

A training-free recursive multiresolution framework for diffeomorphic deformable image registration

no code implementations1 Feb 2022 Ameneh Sheikhjafari, Michelle Noga, Kumaradevan Punithakumar, Nilanjan Ray

The moving image is warped successively at each resolution and finally aligned to the fixed image; this procedure is recursive in a way that at each resolution, a fully convolutional network (FCN) models a progressive change of deformation for the current warped image.

Image Registration

Fully Automated Left Atrium Segmentation from Anatomical Cine Long-axis MRI Sequences using Deep Convolutional Neural Network with Unscented Kalman Filter

no code implementations28 Sep 2020 Xiaoran Zhang, Michelle Noga, David Glynn Martin, Kumaradevan Punithakumar

This study proposes a fully automated approach for the left atrial segmentation from routine cine long-axis cardiac magnetic resonance image sequences using deep convolutional neural networks and Bayesian filtering.

Left Atrium Segmentation Segmentation

Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences

no code implementations18 Aug 2020 Xiaoran Zhang, Michelle Noga, Kumaradevan Punithakumar

The proposed approach is evaluated by the challenge organizers with a test set including 20 cases and achieves a mean dice score of $46. 8\%$ for LV MS and $55. 7\%$ for LV ME+MS

Data Augmentation

A systematic review on the role of artificial intelligence in sonographic diagnosis of thyroid cancer: Past, present and future

no code implementations10 Jun 2020 Fatemeh Abdolali, Atefeh Shahroudnejad, Abhilash Rakkunedeth Hareendranathan, Jacob L. Jaremko, Michelle Noga, Kumaradevan Punithakumar

With more than 50 papers included in this review, we reflect on the trends and challenges of the field of sonographic diagnosis of thyroid malignancies and potential of computer-aided diagnosis to increase the impact of ultrasound applications on the future of thyroid cancer diagnosis.

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