Ms. Kathleen Larson Profile

Kathleen Larson

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Proceedings Article | 3 April 2023 Presentation + Paper

Domain generalization for robust MS lesion segmentation

Huahong Zhang, Hao Li, Kathleen Larson, Kilian Hett, Ipek Oguz

Proceedings Volume 12464, 124640Z (2023) https://doi.org/10.1117/12.2654373

KEYWORDS: Multiple sclerosis, Image segmentation, 3D image processing, Deep learning

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Recently, deep-learning methods have achieved human-level performance on multiple sclerosis (MS) lesion segmentation. However, most established methods are not robust enough for practical use in the real world. They cannot generalize well to images obtained from different clinical sites, or if training and testing datasets contain different MRI modalities. To address these robustness issues, and to bring the deep neural networks closer to clinical use, we propose the addition of data augmentation and modality dropout during training for achieving unsupervised domain generalization. We hypothesize that employing data augmentations can close the gap between different datasets and render the trained models more generalizable. We further hypothesize that the random dropout technique can help the model learn to predict results given any combination of MRI modalities. We conducted an extensive set of comparisons using three publicly available datasets and demonstrate that our method performs better than the baseline without any augmentation and approaches the performance of fully supervised methods. To provide a fair comparison with other MS lesion segmentation methods, we evaluate our methods on the test set of the Longitudinal MS Lesion Segmentation Challenge using the models trained on the other two datasets. The overall score of our approach is substantially higher than the current transfer-learning-based methods and is comparable to the state-of-the-art supervised methods.

Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Synthetic atrophy for longitudinal surface-based cortical thickness measurement

Kathleen Larson, Ipek Oguz

Proceedings Volume 11596, 115963K (2021) https://doi.org/10.1117/12.2580907

KEYWORDS: Image resolution, Tissues, Neuroimaging, Natural surfaces, Image segmentation, Brain

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Difficulty in validating accuracy remains a substantial setback in the field of surface-based cortical thickness (CT) measurement due to the lack of experimental validation against ground truth. Although methods have been developed to create synthetic datasets for this purpose, none provide a robust mechanism for measuring exact thickness changes with surface-based approaches. This work presents a registration-based technique for inducing synthetic cortical atrophy to create a longitudinal, ground truth dataset specifically designed for ac- curacy validation of surface-based CT measurements. Across the entire brain, we show our method can induce up to between 0.6 and 2.6 mm of localized cortical atrophy in a given gyrus depending on the region's original thickness. By calculating the image deformation to induce this atrophy at 400% of the original resolution in each direction, we can induce a sub-voxel resolution amount of atrophy while minimizing partial volume effects. We also show that our method can be extended beyond application to CT measurements for the accuracy validation of longitudinal cortical segmentation and surface reconstruction pipelines when measuring accuracy against cortical landmarks. Importantly, our method relies exclusively on publicly available software and datasets.

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