Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue among different patients and, in many cases, similarity between tumor and normal tissue. We propose a semi-automatic interactive brain tumor segmentation system that incorporates 2D interactive and 3D automatic tools with the ability to adjust operator control. The provided methods are based on an energy that incorporates region statistics computed on available MRI modalities and the usual regularization term. The energy is efficiently minimized on-line using graph cut. Experiments with radiation oncologists testing the semi-automatic tool vs. a manual tool show that the proposed system improves both segmentation time and repeatability.

Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue, among different patients and, in many cases, similarity between tumor and normal tissue. One other challenge is how to make use of prior information about the appearance of normal brain. In this paper we propose a variational brain tumor segmentation algorithm that extends current approaches from texture segmentation by using a high dimensional feature set calculated from MRI data and registered atlases. Using manually segmented data we learn a statistical model for tumor and normal tissue. We show that using a conditional model to discriminate between normal and abnormal regions significantly improves the segmentation results compared to traditional generative models. Validation is performed by testing the method on several cancer patient MRI scans.

Detecting and segmenting brain tumors in magnetic resonance images (MRI) is an important but time-consuming task performed by medical experts. Automating this process is a challenging task due to the often high degree of intensity and textural similarity between normal areas and tumor areas. Several recent projects have explored ways to use an aligned spatial 'template' image to incorporate spatial anatomic information about the brain, but it is not obvious what types of aligned information should be used. This work quantitatively evaluates the performance of 4 different types of alignment-based (AB) features encoding spatial anatomic information for use in supervised pixel classification. This is the first work to (1) compare several types of AB features, (2) explore ways to combine different types of AB features, and (3) explore combining AB features with textural features in a learning framework. We considered situations where existing methods perform poorly, and found that combining textural and AB features allows a substantial performance increase, achieving segmentations that very closely resemble expert annotations.