Tumor Volume

Tumor volume quantification is crucial for cancer diagnosis, prognosis, and treatment monitoring, driving research into accurate and efficient segmentation methods from medical images. Current efforts focus on improving automated segmentation using deep learning architectures like U-Net and its variants, often incorporating techniques like multi-task learning and data augmentation with synthetic tumors to address limitations in real-world datasets. These advancements aim to reduce the time and variability associated with manual segmentation, ultimately improving clinical decision-making and potentially leading to more personalized cancer care.