Interactive level set segmentation for image-guided therapy

Abstract

Image-guided therapy procedures require the patient to remain still throughout the image acquisition, data analysis and therapy. This imposes a tight time constraint on the over-all process. Automatic extraction of the pathological regions prior to the therapy can be faster than the customary manual segmentation performed by the physician. However, the image data alone is usually not sufficient for reliable and unambiguous computerized segmentation. Thus, the oversight of an experienced physician remains mandatory. We present a novel segmentation framework, that allows user feedback. A few mouse-clicks of the user, discrete in nature, are represented as a continuous energy term that is incorporated into a level-set functional. We demonstrate the proposed method on MR scans of uterine fibroids acquired prior to focused ultrasound ablation treatment. The experiments show that with a minimal user input, automatic segmentation results become practically identical to manual expert segmentation.