{'fraction_non_alphanumeric': 0.014661492022423459, 'fraction_numerical': 0.00258732212160414, 'mean_word_length': 5.089238845144357, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Research Statement November 13 , 2007

My research is a collaboration with theorists and end users that produces works of biomedical importance. Image guided diagnostics, therapies, and surgical interventions are improving patients’ prospects and quality of life. These medical applications demand solutions to challenging problems in image analysis and computer vision. Image segmentation, the process of assigning labels to structures in an image, is essential for planning many image-guided medical interventions. It is also an extremely difficult problem. There is no general method to automatically segment any object from any image. Much of my past research has focused on segmentation of the organs in the male pelvis from CT images, to enable image-guided radiation treatment planning for prostate cancer patients. Through my close collaborations with medical physicists and radiation oncologists I learned how they use segmentations for radiation treatment planning and delivery. I also learned the problems that human experts face when producing manual segmentations of the pelvic organs. I communicated those problems to geometers and statisticians in language that these theorists can understand. We developed a theory for segmentation based on geometric models of the organs of interest, statistical models of their variation, and statistical models of image intensity relative to the organs. I have applied these theories to develop tools that my colleagues in medical departments can run. I have taught them the uses and limitations of the tools. As my collaborators and I iterated on this process I learned to recognize problems, in some cases before the end users became aware of them, and recruited the users’ help in resolving the issues. I have three goals for my future research driven by my desire to continue this type of collaboration. First, I will work with medical users to develop a deep understanding of problems that are relevant to their work and that require theoretical advances in image analysis and related fields in order to be solved. Secondly, I will work with the theorists to develop methods and algorithms needed to solve these problems. My third goal is to work with computer scientists to implement solutions to these problems that can be translated back to the medical users.