Rok 2024

• Adaptive Hounsfield Scale Windowing in Computed Tomography Liver Segmentation

  Publikacja

  • J. Cychnerski
  • M. Zakrzewski
  • D. Kwiatkowski

  - Rok 2024

  In computed tomography (CT) imaging, the Hounsfield Unit (HU) scale quantifies radiodensity, but its nonlinear nature across organs and lesions complicates machine learning analysis. This paper introduces an automated method for adaptive HU scale windowing in deep learning-based CT liver segmentation. We propose a new neural network layer that optimizes HU scale window parameters during training. Experiments on the Liver Tumor...

  Pełny tekst do pobrania w serwisie zewnętrznym

Rok 2023

• Comparison of Selected Neural Network Models Used for Automatic Liver Tumor Segmentation

  Publikacja

  • D. Kwiatkowski
  • T. Dziubich

  - Rok 2023

  Automatic and accurate segmentation of liver tumors is crucial for the diagnosis and treatment of hepatocellular carcinoma or metastases. However, the task remains challenging due to imprecise boundaries and significant variations in the shape, size, and location of tumors. The present study focuses on tumor segmentation as a more critical aspect from a medical perspective, compared to liver parenchyma segmentation, which is the...

  Pełny tekst do pobrania w serwisie zewnętrznym