The paper discusses the progress in medical image analysis over the past two decades and the challenges that remain. It highlights the evolution of the field from early pattern recognition techniques to more advanced methods involving 3D image analysis, deformable models, and integration of anatomical and functional information. The paper outlines four key time frames: 1) pre-1980 to 1984, focusing on 2D image analysis; 2) 1985-1991, with knowledge-based strategies and the advent of MRI; 3) 1992-1998, where 3D image analysis became a key goal; and 4) 1999 and beyond, with advanced imaging and computing technology enabling image-guided procedures and realistic visualizations. The paper also discusses the challenges in medical image analysis, including the need for robust algorithms, integration of different data types, and the importance of clinical validation. It emphasizes the importance of combining anatomical and functional information, and the need for more sophisticated methods to handle the complexity of medical images. The paper concludes by noting that while significant progress has been made, there are still many challenges to be addressed in the field of medical image analysis.The paper discusses the progress in medical image analysis over the past two decades and the challenges that remain. It highlights the evolution of the field from early pattern recognition techniques to more advanced methods involving 3D image analysis, deformable models, and integration of anatomical and functional information. The paper outlines four key time frames: 1) pre-1980 to 1984, focusing on 2D image analysis; 2) 1985-1991, with knowledge-based strategies and the advent of MRI; 3) 1992-1998, where 3D image analysis became a key goal; and 4) 1999 and beyond, with advanced imaging and computing technology enabling image-guided procedures and realistic visualizations. The paper also discusses the challenges in medical image analysis, including the need for robust algorithms, integration of different data types, and the importance of clinical validation. It emphasizes the importance of combining anatomical and functional information, and the need for more sophisticated methods to handle the complexity of medical images. The paper concludes by noting that while significant progress has been made, there are still many challenges to be addressed in the field of medical image analysis.