Bayesian segmentation of brainstem structures in MRI

VK: Lampinen, J.In this paper we present a method to segment four brainstem structures (midbrain, pons, medulla oblongata and superior cerebellar peduncle) from 3D brain MRI scans. The segmentation method relies on a probabilistic atlas of the brainstem and its neighboring brain structures. To build the atlas, we combined a dataset of 39 scans with already existing manual delineations of the whole brainstem and a dataset of 10 scans in which the brainstem structures were manually labeled with a protocol that was specifically designed for this study. The resulting atlas can be used in a Bayesian framework to segment the brainstem structures in novel scans. Thanks to the generative nature of the scheme, the segmentation method is robust to changes in MRI contrast or acquisition hardware. Using cross validation, we show that the algorithm can segment the structures in previously unseen T1 and FLAIR scans with great accuracy (mean error under 1 mm) and robustness (no failures in 383 scans including 168 AD cases). We also indirectly evaluate the algorithm with a experiment in which we study the atrophy of the brainstem in aging. The results show that, when used simultaneously, the volumes of the midbrain, pons and medulla are significantly more predictive of age than the volume of the entire brainstem, estimated as their sum. The results also demonstrate that the method can detect atrophy patterns in the brainstem structures that have been previously described in the literature. Finally, we demonstrate that the proposed algorithm is able to detect differential effects of AD on the brainstem structures. The method will be implemented as part of the popular neuroimaging package FreeSurfer.Peer reviewe

Can the Theory of Planned Behavior help explain attendance to follow-up care of childhood cancer survivors?

OBJECTIVE: Childhood cancer survivors are at high risk for late effects. Regular attendance to long-term follow-up care is recommended and helps monitoring survivors' health. Using the Theory of Planned Behavior (TPB) we aimed to 1) investigate the predictors of the intention to attend follow-up care, and 2) examine the associations between perceived control and behavioral intention with actual follow-up care attendance in Swiss childhood cancer survivors. METHODS: We conducted a questionnaire survey in Swiss childhood cancer survivors (diagnosed with cancer aged <16 years between 1990 and 2005; ≥5 years since diagnosis). We assessed TPB-related predictors (attitude, subjective norm, perceived control), intention to attend follow-up care, and actual attendance. We applied structural equation modeling to investigate predictors of intention, and logistic regression models to study the association between intention and actual attendance. RESULTS: Of 299 responders (166 (55.5%) females), 145 (48.5%) reported attending follow-up care. We found that subjective norm, i.e. survivors' perceived social pressure and support, (Coef.0.90, p<0.001) predicted the intention to attend follow-up; attitude and perceived control did not. Perceived control (OR=1.58, 95%CI:1.04-2.41) and intention to attend follow-up (OR=6.43, 95%CI:4.21-9.81) were positively associated with attendance. CONCLUSIONS: To increase attendance, an effort should be made to sensitize partners, friends, parents and health care professionals on their important role in supporting survivors regarding follow-up care. Additionally, interventions promoting personal control over the follow-up attendance might further increase regular attendance

An algorithm for optimal fusion of atlases with different labeling protocols

In this paper we present a novel label fusion algorithm suited for scenarios in which different manual delineation protocols with potentially disparate structures have been used to annotate the training scans (hereafter referred to as "atlases"). Such scenarios arise when atlases have missing structures, when they have been labeled with different levels of detail, or when they have been taken from different heterogeneous databases. The proposed algorithm can be used to automatically label a novel scan with any of the protocols from the training data. Further, it enables us to generate new labels that are not present in any delineation protocol by defining intersections on the underling labels. We first use probabilistic models of label fusion to generalize three popular label fusion techniques to the multi-protocol setting: majority voting, semi-locally weighted voting and STAPLE. Then, we identify some shortcomings of the generalized methods, namely the inability to produce meaningful posterior probabilitiesfor the different labels (majority voting, semi-locally weighted voting) and to exploit the similarities between the atlases (all three methods). Finally, we propose a novel generative label fusion model that can overcome these drawbacks. We use the proposed method to combine four brain MRI datasets labeled with different protocols (with a total of 102 unique labeled structures) to produce segmentations of 148 brain regions. Using cross-validation, we show that the proposed algorithm outperforms the generalizations of majority voting, semi-locally weighted voting and STAPLE (mean Dice score 83%, vs. 77%, 80% and 79%, respectively). We also evaluated the proposed algorithm in an aging study, successfully reproducing some well-known results in cortical and subcortical structures.Peer reviewe

An algorithm for optimal fusion of atlases with different labeling protocols

In this paper we present a novel label fusion algorithm suited for scenarios in which different manual delineation protocols with potentially disparate structures have been used to annotate the training scans (hereafter referred to as “atlases”). Such scenarios arise when atlases have missing structures, when they have been labeled with different levels of detail, or when they have been taken from different heterogeneous databases. The proposed algorithm can be used to automatically label a novel scan with any of the protocols from the training data. Further, it enables us to generate new labels that are not present in any delineation protocol by defining intersections on the underling labels. We first use probabilistic models of label fusion to generalize three popular label fusion techniques to the multi-protocol setting: majority voting, semi-locally weighted voting and STAPLE. Then, we identify some shortcomings of the generalized methods, namely the inability to produce meaningful posterior probabilities for the different labels (majority voting, semi-locally weighted voting) and to exploit the similarities between the atlases (all three methods). Finally, we propose a novel generative label fusion model that can overcome these drawbacks. We use the proposed method to combine four brain MRI datasets labeled with different protocols (with a total of 102 unique labeled structures) to produce segmentations of 148 brain regions. Using cross-validation, we show that the proposed algorithm outperforms the generalizations of majority voting, semi-locally weighted voting and STAPLE (mean Dice score 83%, vs. 77%, 80% and 79%, respectively). We also evaluated the proposed algorithm in an aging study, successfully reproducing some well-known results in cortical and subcortical structures

Perceived information provision and information needs in adolescent and young adult cancer survivors.

Knowledge on former diagnosis, treatment and survivorship is important for adolescent and young adult cancer survivors (AYACS) to make informed healthcare decisions. We aimed to (a) describe the information AYACS reported to have received, (b) identify current information needs and survivors' preferred format of communication, and (c) examine associations between information needs and cancer-related/socio-demographic characteristics, psychological distress and health-related quality of life (HRQoL). We identified AYACS (16-25 years at diagnosis; ≥5 years since diagnosis) through the Cancer Registry Zurich and Zug. Survivors received a questionnaire on information received and current information needs, socio-demographic information, psychological distress (Brief Symptom Inventory-18) and HRQoL (SF-12). Clinical characteristics were available from the cancer registry. We used descriptive statistics and univariable regression models. Of 160 responders, most reported to have received information on disease (96.3%), treatment (96.3%) and follow-up (89.4%), fewer on late effects (63.1%). Survivors reported information needs on late effects (78.7%), follow-up (71.3%), disease (58.1%) and treatment (55.6%). Information needs were associated with experiencing psychological distress and lower mental HRQoL. Most Swiss AYACS have information needs, especially on follow-up and late effects. Therefore, AYACS should be personally, continuously and proactively informed about their disease, treatment, follow-up care and late effects