3D Automatic Segmentation of the Hippocampus Using Wavelets with Applications to Radiotherapy Planning

Abstract

During the past half-century, the cornerstone of treatment for brain metastases has been whole brain irradiation (WBI). WBI has multiple salutary effects including rapid relief of neurological signs and symptoms as well as enhanced local control. Unfortunately, WBI may also engender side effects including memory deficits and decrements in quality of life. Since memory control is thought to be mediated by the hippocampus, attention has been turned to whole brain radiotherapeutic techniques that allow sparing of the hippocampus. In order to be able to minimize dose deposition within the hippocampus, clinicians must be able to confidently identify that structure. However, manually tracing out the hippocampus for each patient is time consuming and subject to individual bias. To this end, an automated method can be very useful for such a task. In this paper, we present a method for extracting the hippocampus from magnetic resonance imaging (MRI) data. Our method is based on a multi-scale shape representation using statistical learning in conjunction with spherical wavelets for shape representation. Indeed, the hippocampus shape information is statistically learned by the algorithm and is further utilized to extract a hippocampus from the given 3D MR image. Results are shown on data-sets provided by Brigham and Women’s Hospital.