<![CDATA[Accurate segmentation of acute intracranial haemorrhage (ICH) in brain computed tomography (CT) scans is crucial for timely diagnosis and effective treatment planning. While the RSNA Intracranial Hemorrhage Detection dataset provides a substantial amount of labeled CT data, most prior research has focused on slice-level classification rather than precise pixel-level segmentation. To address this limitation, a novel segmentation pipeline is proposed that combines a 2.5D U-Net architecture with a dynamic adaptive thresholding technique for enhanced delineation of hemorrhagic lesions and their subtypes. The 2.5D U-Net model leverages spatial continuity across adjacent slices to generate initial lesion probability maps, which are subsequently refined using an adaptive thresholding method that adjusts based on local pixel intensity histograms and edge gradients. Unlike fixed global thresholding approaches such as Otsu’s method, the proposed technique dynamically varies thresholds, enabling more accurate differentiation between hemorrhagic tissue and surrounding brain structures, especially in challenging cases with diffuse or overlapping boundaries. The model was evaluated on carefully selected subsets of the RSNA dataset, achieving a mean Dice similarity coefficient of 0.82 across all ICH subtypes. Compared to standard U-Net and DeepLabV3+ architectures, the hybrid approach demonstrated superior accuracy, boundary precision, and fewer false positives. Visual analysis confirmed more precise lesion delineation and better correspondence with manual annotations, particularly in low-contrast or complex anatomical regions. This integrated approach proves effective for robust segmentation in clinical environments. It holds promise for deployment in computer-aided diagnosis systems, providing radiologists and neurosurgeons with a reliable tool for comprehensive ICH assessment and enhanced decision-making during emergency care]]>
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