Deep learning methods for extraction of neuroimage markers in the prognosis of brain pathologies

• Autores: Albert Clèrigues Garcia
• Directores de la Tesis: Xavier Lladó Bardera (dir. tes.) , Arnau Oliver i Malagelada (codir. tes.) , Sergi Valverde Valverde (codir. tes.)
• Lectura: En la Universitat de Girona ( España ) en 2023
• Idioma: español
• Tribunal Calificador de la Tesis: Gemma Piella (presid.) , Robert Marti Marly (secret.) , Oliver Diaz (voc.)
• Enlaces
  • Tesis en acceso abierto en: TDX
• Resumen

  • This PhD thesis focuses on improving the extraction of neuroimage markers for the prognosis and outcome prediction of neurological pathologies such as ischemic stroke, Alzheimer's disease (AD) and multiple sclerosis (MS). Our work has been developed on two of the most relevant neuroimage markers for diagnosis and prediction, brain lesion segmentation and longitudinal atrophy quantification. Brain lesion segmentation can be directly used in MS and ischemic stroke as a prognostic marker and can also be useful for other downstream segmentation tasks. We also approach the task of brain tissue segmentation for longitudinal atrophy quantification, a validated prognostic image marker in MS and AD. Measurements of longitudinal atrophy can be used to assess the rate of disease progression and might even help to predict AD onset years in advance. In MS patients, an accelerated rate of brain atrophy is also observed as a result of disease activity and is used as a prognostic marker and to evaluate the response of disease-modifying treatments.

    In this thesis, we first approach the task of brain lesion segmentation and propose two patch-based deep learning methods for ischemic stroke, a 2D approach for computed tomography (CT) images and a 3D one for magnetic resonance imaging (MRI). The proposed approaches have shown state-of-the-art performance on two well-known publicly available datasets from the 2015 and 2018 editions of the Ischemic Stroke Lesion Segmentation (ISLES) challenge.

    In the subsequent stages of this thesis, we focused on brain tissue segmentation for cross-sectional and longitudinal volumetric analysis. We propose an unsupervised patch-based deep learning framework designed for accurate brain tissue segmentation which does not rely on manual annotations for training. Although the effect of WM lesions typically observed in MS patient images has been extensively studied in classical brain tissue segmentation methods, it has still not been evaluated within the more recent deep learning based approaches. In this regard, we begin by studying and evaluating the error that is introduced by WM lesions in our deep learning based tissue segmentation framework. Then, we propose an approach to reduce the error that these lesions introduce in the measured tissue volumes.

    Finally, we propose a deep learning method for longitudinal atrophy quantification. Within our approach, the network learns from a reference tissue segmentation method while utilizing data priors to regularize the training and avoid learning its errors and biases. The experimental results show our method has greatly reduced short interval error and improved sensitivity to differences between healthy and AD patients compared to the reference method used for training.

    In this PhD thesis, we have worked with diverse neuroimage markers and imaging modalities, which has provided valuable insights on the issues and challenges for their use in prognostic and predictive tasks.