Lesion Wise Metric

Lesion-wise metrics evaluate the accuracy of medical image segmentation by assessing performance on individual lesions rather than the entire image, focusing on metrics like Dice Similarity Coefficient and Hausdorff Distance to quantify the agreement between automated and expert-defined lesion boundaries. Current research emphasizes the development and application of deep learning models, including U-Net architectures and Vision Transformers, for improved lesion segmentation and subsequent lesion-wise quantification, particularly within the context of brain tumor segmentation and other challenging medical imaging tasks. This focus on lesion-level accuracy is crucial for improving diagnostic precision, treatment planning, and the objective assessment of disease progression and treatment response in various clinical settings.