Recently, the high spatial resolution and soft-tissue contrast offered by MRI makes it the most accurate method available for obtaining this kind of information. This, combined with the potential of MRI to localize and grade prostate cancer, has led to a rapid increase in its adoption and increasing research interest in its use for this application. Furthermore, the medical image analysis and clinical community has been very interested in developing accurate prostate MRI segmentation methods which apart from accurate, automated prostate volume segmentation also serve an important purpose for computer-aided detection and diagnostic algorithms, as well as a number of multi-modality image registration algorithms, which aim to enable MRI-derived information on anatomy and tumour location and extent to aid therapy planning and guidance.

The prostate gland consists of internal structures including the peripheral zone (PZ), central zone (CZ), and transition zone (TZ), where the latter 2 structures are jointly referred to as the central gland (CG). While most tumors are found in the PZ, tumors can also be found in the CG, and CG tumors can have drastically different appearance than PZ tumors. In the PZ (where most cancers are found), tumors are typically chacterized by hypointense regions on MRI images, in stark contrast to the usually hyperintense PZ regions. However, in the CG, tumors are typically noticeable due to their homogeneous texture, as compared to the traditionally hetergeneous texture in the CG. In recent years, several computer aided detection (CAD) systems have been developed for detecting tumors from prostate MRI imagery. Since the tumors in the PZ can appear drastically different from tumors in the CT, CAD systems would invariably benefit from knowing where each internal prostate structure was located.

In addition, treatment options can even be tailored to an individual patient, as CG tumors have been found to be significantly less aggressive compared to PZ tumors. However, most extant prostate segmentation systems only consider the prostate capsule boundary. A methodology to automatically and simultaneously segment the prostate, PZ, and CG from T2-weighted MRI alone could also allow for the development of a spatially aware CAD system for prostate cancer detection, by leveraging the explicit, automated segmentations of the different prostate zones. A second application is to create patient-specific treatment models based on the zonal location of the tumor.
This year's ISBI challenges participants to outline those two non-overlapping adjacent regions of the gland. Advancing solutions to this vexing clinical problem will surely gain recognition of the power of computer image processing to address a widespread but critical healthcare puzzle. Algorithms applicable to both 1.5T and 3T will be most welcome since the scanner installed base is presently mostly 1.5T.