Prediction of treatment response from retinal OCT in patients with exudative age-related macular degeneration

Age related macular degeneration is a major cause of blindness and visual impairment in older adults. Its exudative form, where fluids leak into the macula, is especially damaging. The standard treatment involves injections of anti-VEGF (vascular endothelial growth factor) agents into the eye, which prevent further vascular growth and leakage, and can restore vision. These intravitreal injections have a risk of devastating complications including blindness from infection and are expensive. Optimizing the interval between injections in a patient specific manner is of great interest, as the retinal response is partially patient specific. In this paper we propose a machine learning approach to predict the retinal response at the end of a standardized 12-week induction phase of the treatment. From a longitudinal series of optical coherence tomography (OCT) images, a number of quantitative measurements are extracted, describing the underlying retinal structure and pathology and its response to initial treatment. After initial feature selection, the selected set of features is used to predict the treatment response status at the end of the induction phase using the support vector machine classifier. On a population of 30 patients, leave-one-out cross-validation showed the classification success rate of 87% of predicting whether the subject will show a response to the treatment at the next visit. The proposed methodology is a promising step towards the much needed image-guided prediction of patient-specific treatment response

Transformer-based end-to-end classification of variable-length volumetric data

The automatic classification of 3D medical data is memory-intensive. Also, variations in the number of slices between samples is common. Naive solutions such as subsampling can solve these problems, but at the cost of potentially eliminating relevant diagnosis information. Transformers have shown promising performance for sequential data analysis. However, their application for long-sequences is data, computationally, and memory demanding. In this paper, we propose an end-to-end Transformer-based framework that allows to classify volumetric data of variable length in an efficient fashion. Particularly, by randomizing the input slice-wise resolution during training, we enhance the capacity of the learnable positional embedding assigned to each volume slice. Consequently, the accumulated positional information in each positional embedding can be generalized to the neighbouring slices, even for high resolution volumes at the test time. By doing so, the model will be more robust to variable volume length and amenable to different computational budgets. We evaluated the proposed approach in retinal OCT volume classification and achieved 21.96% average improvement in balanced accuracy on a 9-class diagnostic task, compared to state-of-the-art video transformers. Our findings show that varying the slice-wise resolution of the input during training results in more informative volume representation as compared to training with fixed number of slices per volume. Our code is available at: https://github.com/marziehoghbaie/VLFAT

Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data

Robust forecasting of the future anatomical changes inflicted by an ongoing disease is an extremely challenging task that is out of grasp even for experienced healthcare professionals. Such a capability, however, is of great importance since it can improve patient management by providing information on the speed of disease progression already at the admission stage, or it can enrich the clinical trials with fast progressors and avoid the need for control arms by the means of digital twins. In this work, we develop a deep learning method that models the evolution of age-related disease by processing a single medical scan and providing a segmentation of the target anatomy at a requested future point in time. Our method represents a time-invariant physical process and solves a large-scale problem of modeling temporal pixel-level changes utilizing NeuralODEs. In addition, we demonstrate the approaches to incorporate the prior domain-specific constraints into our method and define temporal Dice loss for learning temporal objectives. To evaluate the applicability of our approach across different age-related diseases and imaging modalities, we developed and tested the proposed method on the datasets with 967 retinal OCT volumes of 100 patients with Geographic Atrophy, and 2823 brain MRI volumes of 633 patients with Alzheimer's Disease. For Geographic Atrophy, the proposed method outperformed the related baseline models in the atrophy growth prediction. For Alzheimer's Disease, the proposed method demonstrated remarkable performance in predicting the brain ventricle changes induced by the disease, achieving the state-of-the-art result on TADPOLE challenge

Geometric modeling and characterization of the circle of willis

Los derrames cerebrales son una de las causas principales de morbilidad y mortalidad en los países desarrollados. Esto ha motivado una búsqueda de configuraciones del sistema vascular que se cree que están asociadas con el desarrollo de enfermedades vasculares. En la primera contribución se ha mejorado un método de segmentación vascular para lograr robustez en la segmentación de imágenes procedentes de diferentes modalidades y centros clínicos, con una validación exhaustiva. Una vez que el sistema vascular está correctamente segmentado, en la segunda contribución se ha propuesto una metodología para caracterizar ampliamente la geometría de la arteria carótida interna (ACI). Esto ha incluido el desarrollo de un método para identificar automáticamente la ACI a partir del árbol vascular segmentado. Finalmente, en la tercera contribución, esta identificación automática se ha generalizado a una colección de arterias incluyendo su conectividad y sus relaciones topológicas. Finalmente, la identificación de las arterias en un conjunto de individuos puede permitir la comparación geométrica de sus árboles arteriales utilizando la metodología introducida para la caracterización de la ACI.Stroke is among the leading causes of morbidity and mortality in the developed countries. This motivated a search for the configurations of vasculature that is assumed to be associated with the development of vascular diseases. In the first contribution we improve a vascular segmentation method to achieve robustness in segmenting images coming from different imaging modalities and clinical centers and we provide exhaustive segmentation validation. Once the vasculature is successfully segmented, in the second contribution we propose a methodology to extensively characterize the geometry of the internal carotid artery (ICA). This includes the development of a method to automatically identify the ICA from the segmented vascular tree. Finally in the third contribution, this automatic identification is generalized to a collection of vessels including their connectivity and topological relationships. Identifying the corresponding vessels in a population enables comparison of their geometry using the methodology introduced for the characterization of the ICA