Browsing by Author "Tzolova, Bilyana"

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    Coupled Flow and Transport in an Organ and its Vasculature
    (2024-08-08) Tzolova, Bilyana; Riviere, Beatrice; Fuentes, David
    In contrast to many other types of cancer, the incidence of liver cancer, specifically hepatocellular carcinoma (HCC), is on the rise. For most patients, surgical intervention is not a viable option, leaving them reliant on chemotherapy treatments, particularly transarterial chemoembolization (TACE), for relief. Our study aims to understand how these treatments function within the liver and their impact on tumor growth. Building upon existing research, we model the flow and transport of chemotherapy drugs and embolic agents in the liver using the miscible displacement equations. Utilizing CT images from liver cancer patients, we extract a 1D centerline of the hepatic vascular structures that deliver blood to the tumors, and then construct a 3D mesh from the liver segmentations. We employ the singularity subtraction technique to create a finite element model for the flow of blood in the liver, specifically focusing on areas affected by the TACE treatment. We extend the singularity subtraction technique to the time-dependent advection-diffusion equation to model the concentration of chemotherapy drugs in the liver and tumors. We first solve the time-dependent non-conservative advection-diffusion equation using the finite element method. To address instabilities arising when the model is advection dominated, we then utilize the discontinuous Galerkin method to solve the time-dependent conservative advection-diffusion equation. We couple the models for blood flow following the injection of an embolic agent with the transport of chemotherapy to develop a comprehensive model based on the miscible displacement equations in the liver. We then apply the simulation to data from MD Anderson patients diagnosed with hepatocellular carcinoma who have undergone transarterial chemoembolization treatment. This final model enables us to provide insights into the evolving dynamics of TACE within the liver.
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    Machine Learning Methods for Vessel Segmentation in Organs
    (2022-04-19) Tzolova, Bilyana; Riviere, Beatrice; Fuentes, David
    The vascular system plays a crucial role in diagnostics, treatment, and surgical planning in a wide array of diseases. Recently, there has been a growing interest in automating the manual vessel segmentation process to save time. We aim to efficiently and effectively segment the vascular system in the liver organ using deep learning techniques in order to improve on current manual methods. We propose a 3D DenseNet using PocketNet paradigm with binary and ternary classifications that has less parameters to train than the state of the art methods. We explore the impact of various preprocessing techniques on the accuracy of the neural network. We are able to reduce training times and increase accuracy per training parameter in medical imaging segmentation of the liver vessels. Finally, we assess the accuracy of our model predictions using the dice score coefficient. We find that successful preprocessing filters and neural network parameters are necessary for consistently high dice scores.