Abstract:

Brain tumour segmentation is a key application of AI in neuroimaging. Recently, federated learning (FL) has emerged as a strategic and increasingly relevant paradigm in neural computing due to its ability to address key challenges in large-scale neural network training, such as data access, privacy, collaborative learning, and model robustness. However, its adoption is currently hindered by high communication costs and the heterogeneity of client data. In this study, we investigated an efficient FL framework for brain tumour segmentation based on communication-aware optimization. We evaluated FedWSOComp, which integrates sparsification, quantization, and entropy-based encoding, in combination with a 3D U-Net architecture under both homogeneous and heterogeneous data distributions. The multi-institutional FeTS 2024 dataset was employed and partitioned into independent and identically distributed (IID) and non-IID settings, with an independent test set of 67 patients. An overall of 18 configurations combined sparsification rates and quantization levels. Performance was measured using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95). Experimental results demonstrated that aggressive compression caused severe degradation in segmentation quality, with HD95 exceeding 60 mm. In contrast, higher retention with finer quantization achieved the best balance between efficiency and accuracy, reaching a DSC and HD95 mm on the test set under non-IID conditions. The findings demonstrated that, when configured with moderate-to-fine quantization and high sparsification retention, FedWSOComp enabled accurate and communication-efficient federated brain tumour segmentation. This study provides quantitative evidence and practical guidance for the deployment of FL-based segmentation models in privacy-sensitive and bandwidth-constrained clinical settings.

Abstract:

Atrial fibrillation (AFib) is the most common clinically relevant cardiac arrhythmia. A promising treatment modality is pulsed field ablation (PFA), a non-thermal catheter ablation technique that offers increased tissue selectivity and a reduced risk of collateral damage compared to thermal ablation methods. PFA induces irreversible electroporation, whereby electric pulses create pores in the cell membrane, ultimately leading to cell death. While electroporation depends on pulse parameters, field strength, and cell orientation, previous studies have focused on single cells. This work addresses the question: How do orientation and pulse parameters affect electroporation in coupled cardiomyocytes? This thesis investigates the influence of anisotropy on electroporation through development and extension of a unicellular model to two-cell and five-cell models of electrically coupled cardiomyocytes. Electrical coupling between the cells was modelled by incorporating gap junction conductivity and its reduction due to electroporation. Finite element simulations were performed for six different pulse durations (100 ns, 1 μs, 10 μs, 100 μs, 1 ms, 10 ms) and electric field strengths in the range 10 to 10^5 V/cm for both parallel and perpendicular cell orientations relative to the electric field. The contribution of gap junctions to the induced TMV and pore density was assessed by comparing simulations with functional and inhibited gap junctions. The two-cardiomyocyte model exhibited electroporation onset and extent consistent with unicellular studies, with pore formation occurring at lower field strengths in the parallel orientation for pulse durations of 100 μs and longer. In contrast, the five-cardiomyocyte model demonstrated electroporation only for 1 ms and 10 ms pulses at field strengths exceeding 10^4 V/cm, with shorter pulses failing to induce electroporation. In this five-cell configuration, electroporation onset was unaffected by cell orientation, though orientation-dependent differences in electroporation extent persisted. Gap junction mediated intercellular coupling influenced membrane voltage approximately twice as strongly when the intercalated disc was aligned parallel to the electric field. Key limitations of the model include the use of a fixed, real-valued membrane capacitance, and the simplified representation of the intercalated disc as a single-capacitor element. These simplifications led to altered TMV dynamics for nanosecond pulses and exaggerated immediate effects of gap junction decoupling. Overall, this work provides insight into anisotropic electroporation in coupled cardiomyocytes and demonstrates the importance of incorporating multicellular effects when designing pulse protocols. The findings suggest that short pulses reduce orientation dependence and may improve lesion uniformity, while gap junction coupling modulates electroporation thresholds and extent. By linking unicellular phenomena to multicellular behaviour, this thesis offers a foundation for optimising PFA protocols to achieve more predictable and effective ablation outcomes.

Abstract:

The extracellular–membrane–intracellular (EMI) model enables cardiac electrophysiologysimulations with explicit resolution of individual cardiomyocytes and their microstructuralinteractions. By directly representing intracellular, extracellular, and membrane domains, theEMI framework allows the investigation of cell-level conduction mechanisms that cannot becaptured by homogenized mono- or bidomain models. However, this increased physiologicalfidelity comes at the cost of substantially higher computational demands due to the largenumber of degrees of freedom required for spatial discretization.The main objective of this bachelor’s thesis is to systematically investigate the influenceof spatial mesh resolution on simulation accuracy and computational performance in EMIbased cardiac electrophysiology simulations using the openCARP framework. For thispurpose, a convergence analysis was performed. Starting from a high-resolution referencemesh, a large set of coarsened meshes was generated by varying edge length constraintsand geometric deviation parameters. To assess numerical convergence, three clinically andphysiologically relevant metrics were analyzed: unipolar electrograms, conduction velocities(CVs), and upstroke velocities of the action potentials. The finest mesh configuration servedas a reference solution against which all other configurations were compared.The results demonstrated that substantial reductions in computational cost can be achievedthrough spatial coarsening while maintaining acceptable accuracy, defined by individuallimits depending on the chosen metric. For the optimal mesh configuration with a meaneffective edge length of hedge = 10.3µm (σedge = 2.98µm), computational runtime wasreduced by approximately 80% compared to the highest-resolution reference mesh whilepreserving electrophysiological accuracy for all considered metrics. Based on the meshconfiguration identified as the optimal trade-off for CV evaluation, a parameter tuningstudy was conducted to obtain physiologically realistic longitudinal CV values within theEMI framework in the openCARP environment. Specifically, physiologically realisticlongitudinal conduction velocities of approximately 1m/s were obtained by selecting eithera gap junction resistance of Rg = 0.00613kΩcm2 at an intracellular conductivity of σi =0.4S/m, or an intracellular conductivity of σi = 0.1575S/m at a gap junction resistance ofRg =0.003kΩcm2.Overall, this work provides practical guidelines for selecting mesh resolution and parametervalues in EMI-based simulations and contributes to the validation and efficient application ofmicrostructural cardiac electrophysiology models.