3 research outputs found
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning
In this paper we present a method for simultaneously segmenting brain tumors
and an extensive set of organs-at-risk for radiation therapy planning of
glioblastomas. The method combines a contrast-adaptive generative model for
whole-brain segmentation with a new spatial regularization model of tumor shape
using convolutional restricted Boltzmann machines. We demonstrate
experimentally that the method is able to adapt to image acquisitions that
differ substantially from any available training data, ensuring its
applicability across treatment sites; that its tumor segmentation accuracy is
comparable to that of the current state of the art; and that it captures most
organs-at-risk sufficiently well for radiation therapy planning purposes. The
proposed method may be a valuable step towards automating the delineation of
brain tumors and organs-at-risk in glioblastoma patients undergoing radiation
therapy.Comment: corrected one referenc
A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning
In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy
A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning
In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of
organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive
generative model for whole-brain segmentation with a new spatial regularization model of tumor shape
using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is
able to adapt to image acquisitions that differ substantially from any available training data, ensuring its
applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the
current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy
planning purposes. The proposed method may be a valuable step towards automating the delineation of
brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy