A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients

We present a fully automated generative method for simultaneous brain tumor and organs-at-risk segmentation in multi-modal magnetic resonance images. The method combines an existing whole-brain segmentation technique with a spatial tumor prior, which uses convolutional restricted Boltzmann machines to model tumor shape. The method is not tuned to any specific imaging protocol and can simultaneously segment the gross tumor volume, peritumoral edema and healthy tissue structures relevant for radiotherapy planning. We validate the method on a manually delineated clinical data set of glioblastoma patients by comparing segmentations of gross tumor volume, brainstem and hippocampus. The preliminary results demonstrate the feasibility of the method

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

Combined economic emission dispatch problem using chaotic self adaptive PSO

This research work presents a Chaotic Self Adaptive Particle Swarm Optimization (CSAPSO) algorithm in order to solve the Combined Economic Emission Dispatch (CEED) problem. The main purpose of the work is to derive a simple and effective method for optimum generation dispatch to minimize the fuel cost and emission of power networks by considering several non-linear characteristics of the generator such as valve point effect, prohibited operating zones and ramp rate limits. A chaotic local search operator is introduced in the proposed algorithm to avoid premature convergence. Simulation studies are carried out, using MATLAB software, to show the effectiveness of the proposed optimization method. The applicability and high feasibility of the proposed method is validated on IEEE 30 bus, six generator systems. The CSAPSO based approach has been extended to evaluate the trade-off curve between the fuel cost and emission according to the bi-criterion objective function. In order to see the effectiveness of the proposed algorithm, it has been compared with other algorithms in the literature. Results show that the CSAPSO is more powerful than other algorithms