Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

The impact of retrieval processes, age, general achievement level, and test scoring scheme for children's metacognitive monitoring and controlling

This multi-phase study examined the influence of retrieval processes on children’s metacognitive processes in relation to and in interaction with achievement level and age. First, N = 150 9/10- and 11/12-year old high and low achievers watched an educational film and predicted their test performance. Children then solved a cloze test regarding the film content including answerable and unanswerable items and gave confidence judgments to every answer. Finally, children withdrew answers that they believed to be incorrect. All children showed adequate metacognitive processes before and during test taking with 11/12- year-olds outperforming 9/10-year-olds when considering characteristics of on-going retrieval processes. As to the influence of achievement level, high compared to low achievers proved to be more accurate in their metacognitive monitoring and controlling. Results suggest that both cognitive resources (operationalized through achievement level) and mnemonic experience (assessed through age) fuel metacognitive development. Nevertheless, when facing higher demands regarding retrieval processes, experience seems to play the more important role.