Terry Tsebro

This work builds MyoSTAT.AI, a deterministic and fully reproducible benchmarking framework for cardiac ultrasound segmentation and shear-wave elastography (SWE) velocity-field stabilization evaluated entirely on synthetic data, comparing four U-Net-based architectures (2D, 2.5D, 3D, ConvLSTM) through single-variable ablation and six stabilization methods, and reports that the ConvLSTM variant achieved the highest segmentation accuracy (Dice 0.994, IoU 0.987, an 18.6 percentage-point gain over the 2D baseline Dice 0.808), that UNet2.5D with a 3-frame temporal window reached Dice 0.983 at lower latency (433 ms vs. 1193 ms), that TensorRT FP16 deployment sustained 341 FPS on an RTX 3060 and 90.

This work builds MyoSTAT.AI, a deterministic and fully reproducible benchmarking framework for cardiac ultrasound segmentation and shear-wave elastography (SWE) velocity-field stabilization evaluated entirely on synthetic data, comparing four U-Net-based architectures (2D, 2.5D, 3D, ConvLSTM) through single-variable ablation and six stabilization methods, and reports that the ConvLSTM variant achieved the highest segmentation accuracy (Dice 0.994, IoU 0.987, an 18.6 percentage-point gain over the 2D baseline Dice 0.808), that UNet2.5D with a 3-frame temporal window reached Dice 0.983 at lower latency (433 ms vs. 1193 ms), that TensorRT FP16 deployment sustained 341 FPS on an RTX 3060 and 90.