Tumor Shape

Tumor shape analysis is crucial for cancer diagnosis, treatment planning, and prognosis, focusing on accurately characterizing tumor morphology and its changes over time. Current research employs advanced deep learning models, including 3D convolutional neural networks (like U-Net and variations), recurrent neural networks, and transformer architectures, often combined with deformable image registration techniques, to analyze medical images (CT, MRI) and predict tumor growth. These methods aim to improve the accuracy and efficiency of tumor segmentation, volume measurement, and the prediction of treatment response, ultimately leading to more personalized and effective cancer care. The development of robust and automated methods for tumor shape analysis holds significant promise for advancing cancer research and improving patient outcomes.