Simultaneous registration and landmark detection.
We are developing a system for patient management in colorectal cancer, in which a difficult case of non-rigid registration, namely of pre- and post-therapy images, arises. Numerous non-rigid registration algorithms have been proposed in medical image analysis, and we have applied several leading algorithms to our non-rigid registration problem; but with unpromising results. The fundamental reason appears to be that they lack with knowledge of the particular application. We propose a graphical representation of anatomical knowledge relevant for colorectal cancer, and of the ways in which this anatomy may be predicted to change as a result of chemo and radiotherapy. We show how we interleave this representation with an adaptive registration algorithm to make the non-rigid registration result both robust and accurate.
Bond
Sarah L
SL
Wolfson Med. Vision Lab., Oxford Univ., UK. sarah@robots.ox.ac.uk
Brady
Michael
M
D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique