Publications by authors named "T J Lustberg"
Atlas-based automatic segmentation is used in radiotherapy planning to accelerate the delineation of organs at risk (OARs). Atlas selection has been proposed as a way to improve the accuracy and execution time of segmentation, assuming that, the more similar the atlas is to the patient, the better the results will be. This paper presents an analysis of atlas selection methods in the context of radiotherapy treatment planning.
View Article and Find Full Text PDFPurpose: Automated techniques for estimating the contours of organs and structures in medical images have become more widespread and a variety of measures are available for assessing their quality. Quantitative measures of geometric agreement, for example, overlap with a gold-standard delineation, are popular but may not predict the level of clinical acceptance for the contouring method. Therefore, surrogate measures that relate more directly to the clinical judgment of contours, and to the way they are used in routine workflows, need to be developed.
View Article and Find Full Text PDFAtlas-based segmentation is used in radiotherapy planning to accelerate the delineation of organs at risk (OARs). Atlas selection has been proposed to improve the performance of segmentation, assuming that the more similar the atlas is to the patient, the better the result. It follows that the larger the database of atlases from which to select, the better the results should be.
View Article and Find Full Text PDFArticle Synopsis
- This study compares various machine learning classification algorithms to predict treatment responses in (chemo)radiotherapy, focusing on their effectiveness in handling data from different tumor types.
- Twelve datasets involving 3,496 patients were analyzed using six machine learning classifiers, and their performance was evaluated through multiple metrics such as accuracy and AUC (Area Under the Curve).
- The findings indicate that specific classifiers can significantly improve predictions for new datasets compared to random selection, highlighting the importance of choosing the right algorithm for better treatment outcome predictions.