Orbes Arteaga, Mauricio

English

Quantiﬁcation of white matter hyperintensities (WMH) is necessary to understand their role in several neurological diseases. Measurements such as volumetric esti-mations need to be accurate enough to provide valid insights. Consequently, robust and accurate segmentation of WMH is needed. Modern machine learning techniques such as Deep Learning (DL) have made important advances in this ﬁeld, showing unprecedented performance in segmentation. However, their applicability in realistic clinical scenarios is still questioned due to their lack of generalization capabilities when trained on limited amounts of data. This problem could be more notable in segmentation of anomalies such WMHs. Therefore in this thesis, I focus on the three main challenges that make the task of WMHs segmentation more difﬁcult: i) Inter-modality heterogeneity –the number and type of modalities to analyzed WMHs can vary from center to center, where less expensive to acquired modalities could not be always available for every single patient; ii) morphological heterogeneity –WMHs present diverse shapes and appearances, therefore, learn invariant features to these variations requires large and morphological diverse datasets; iii) intra-modality heterogeneity –scanners or acquisition protocols can be different from one center to another, therefore, segmenting images acquired at a different center than the one used for model training can result in an inaccurate segmentation.In this thesis, I proposed different approaches to tackle the aforementioned challenges. Firstly, I address inter modality-heterogeneity; I proposed an efﬁcient strategy to leverage information from all available modalities (at the training stage) with the ultimate end of improving segmentation performance on models that only can use more simplistic modalities as input. Next, I proposed a data augmentation approach to increase morphological variability in the training sets; Thus, models can learn features that are robust to this type of variation. To deal with intra-modality heterogeneity, I proposed two different approaches for unsupervised domain adaptation. The ﬁrst approach proposed a simple but effective strategy based on Knowledge Distillation (KD) to transfer information from labeled (source domain) to unlabeled data (target domain). The second approach extends this idea by introducing data augmentation and consistency training to encourage robustness to different levels of noise on the data. I believe that the proposed methodologies offer an important contribution to the medical imaging ﬁeld by providing solutions that improve the performance of Deep Learning approaches in realistic clinical settings

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Robust White Matter Hyperintensity Segmentation

https://kclpure.kcl.ac.uk/portal/files/238670721/2022_Orbes_Arteaga_Henry_180000022_ethesis.pdf

Robust White Matter Hyperintensity Segmentation

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