Optimization Strategies for Interactive Classification of Interstitial Lung Disease Textures

For computerized analysis of textures in interstitial lung disease, manual annotations of lung tissue are necessary. Since making these annotations is labor intensive, we previously proposed an interactive annotation framework. In this framework, observers iteratively trained a classifier to distinguish the different texture types by correcting its classification errors. In this work, we investigated three ways to extend this approach, in order to decrease the amount of user interaction required to annotate all lung tissue in a computed tomography scan. First, we conducted automatic classification experiments to test how data from previously annotated scans can be used for classification of the scan under consideration. We compared the performance of a classifier trained on data from one observer, a classifier trained on data from multiple observers, a classifier trained on consensus training data, and an ensemble of classifiers, each trained on data from different sources. Experiments were conducted without and with texture selection (ts). In the former case, training data from all eight textures was used. In the latter, only training data from the texture types present in the scan were used, and the observer would have to indicate textures contained in the scan to be analyzed. Second, we simulated interactive annotation to test the effects of (1) asking observers to perform ts before the start of annotation, (2) the use of a classifier trained on data from previously annotated scans at the start of annotation, when the interactive classifier is untrained, and (3) allowing observers to choose which interactive or automatic classification results they wanted to correct. Finally, various strategies for selecting the classification results that were presented to the observer were considered. Classification accuracies for all possible interactive annotation scenarios were compared. Using the best-performing protocol, in which observers select the textures that should be distinguished in the scan and in which they can choose which classification results to use for correction, a median accuracy of 88% was reached. The results obtained using this protocol were significantly better than results obtained with other interactive or automatic classification protocols

Metabolic syndrome, prediabetes, and brain abnormalities onmri in patients with manifest arterial disease: The smart-mr study

OBJECTIVE: Metabolic syndrome (MetS) is a cluster of cardiovascular risk factors leading to atherosclerosis and diabetes. Diabetes is associated with both structural and functional abnormalities of the brain. MetS, even before diabetes is diagnosed, may also predispose to cerebral changes, probably through shared mechanisms. We examined the association of MetS with cerebral changes in patients with manifest arterial disease. RESEARCH DESIGN AND METHODS Cross-sectional data on MetS and brain MRI were available in 1,232 participants withmanifest arterial disease (age 58.6610.1 years; 37%MetS). Volumes of brain tissue, ventricles, and white matter hyperintensities (WMH) were obtained by automated segmentation and expressed relative to intracranial volume. Infarcts were distinguished into lacunar and nonlacunar infarcts. RESULTS: The presence of MetS (n = 451) was associated with smaller brain tissue volume (B 20.72% [95% CI 20.97, 20.47]), even in the subgroup of patients without diabetes (B 20.42% [95% CI 20.71, 20.13]). MetS was not associated with an increased occurrence of WMH or cerebral infarcts. Impaired glucose metabolism, abdominal obesity, and elevated triglycerides were individual components associated with smaller brain volume. Obesity and hypertriglyceridemia remained associated with smaller brain volume when patients with diabetes were excluded. Hypertension was associated with an increased occurrence of WMH and infarcts. CONCLUSIONS: In patients with manifest arterial disease, presence of MetS is associated with smaller brain volume, even in patients without diabetes. Screening for MetS and treatment of its individual components, in particular, hyperglycemia, hypertriglyceridemia, and obesity, may prevent progression of cognitive aging in patients with MetS, even in a prediabetic stage

Semi-automatic classification of textures in thoracic CT scans

The textural patterns in the lung parenchyma, as visible on computed tomography (CT) scans, are essential to make a correct diagnosis in interstitial lung disease. We developed one automatic and two interactive protocols for classification of normal and seven types of abnormal lung textures. Lungs were segmented and subdivided into volumes of interest (VOIs) with homogeneous texture using a clustering approach. In the automatic protocol, VOIs were classified automatically by an extra-trees classifier that was trained using annotations of VOIs from other CT scans. In the interactive protocols, an observer iteratively trained an extra-trees classifier to distinguish the different textures, by correcting mistakes the classifier makes in a slice-by-slice manner. The difference between the two interactive methods was whether or not training data from previously annotated scans was used in classification of the first slice. The protocols were compared in terms of the percentages of VOIs that observers needed to relabel. Validation experiments were carried out using software that simulated observer behavior. In the automatic classification protocol, observers needed to relabel on average 58% of the VOIs. During interactive annotation without the use of previous training data, the average percentage of relabeled VOIs decreased from 64% for the first slice to 13% for the second half of the scan. Overall, 21% of the VOIs were relabeled. When previous training data was available, the average overall percentage of VOIs requiring relabeling was 20%, decreasing from 56% in the first slice to 13% in the second half of the scan

Diabetes increases atrophy and vascular lesions on brain MRI in patients with symptomatic arterial disease

Background and Purpose - Diabetes type 2 (DM2) is associated with accelerated cognitive decline and structural brain abnormalities. Macrovascular disease has been described as a determinant for brain MRI changes in DM2, but little is known about the involvement of other DM2-related factors. Methods - Brain MRI was performed in 1043 participants (151 DM2) with symptomatic arterial disease. Brain volumes were obtained through automated segmentation. Results - Patients with arterial disease and DM2 had more global and subcortical brain atrophy (-1.20% brain/intracranial volume [95% CI -1.58 to - 0.82], P <0.0005 and 0.20% ventricular/intracranial volume [0.05 to 0.34], P <0.01), larger WMH volumes (0.22 logtransformed volume [0.07 to 0.38], P <0.005), and more lacunar infarcts (OR 1.75 [1.13 to 2.69], P <0.01) than identical patients without DM2. In patients with DM2, high glucose levels (B - 0.12% per mmol/L [-0.23 to -0.01], P <0.05) and diabetes duration (B - 0.05% per year [-0.10 to -0.001], P <0.05) were associated with global brain atrophy. Conclusion - In patients with symptomatic arterial disease, DM2 has an added detrimental effect on the brain. In patients with DM2, hyperglycemia and diabetes duration contribute to brain atrophy