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Query: UMLS:C0851184 (thinning)
 11,252 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Perforator flaps have been increasingly used in the past few years for trauma and reconstructive surgical cases. With the thinned flap design, greater survivability and a decrease in donor site morbidity have been reported. Knowledge of the 3D vascular tree will provide insight information about the dissection region, vascular territory, and fascia levels. In this paper, we will propose a computational framework for the automatic 3D vascular tree construction. The computational framework begins with an image segmentation algorithm, spedge-and-medge, which is an integration of Canny edge detector, edge-linking, and split-and-merge to initially segment out the vessels from the background. To deal with the possible broken vessels, a vascular cross-sectional tree repairing and interpolation algorithm is then developed based on the 3D connectivity and root-converging properties of the tree branches. Furthermore, to extract the essential characteristics of the vascular structure, 3D thinning algorithms are used to build up the skeletons of the tree. At each stage of the framework, 3D rendering results are provided for the visualization of the computed results. The proposed method achieves good performance and has been used for the 3D vascular tree construction and surgical danger zone measurements on 39 harvested cadaver perforator flaps with the types of ALTP, GAP, and TAP.
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PMID:Automatic 3D vascular tree construction of perforator flaps for plastic surgery planning. 1727 Oct 20

The diagnosis of schizophrenia is thought to embrace several distinct subgroups. The manifold entities in a single clinical patient group increase the variance of biological measures, deflate the group-level estimates of causal factors, and mask the presence of treatment effects. However, reliable neurobiological boundaries to differentiate these subgroups remain elusive. Since cortical thinning is a well-established feature in schizophrenia, we investigated if individuals (patients and healthy controls) with similar patterns of regional cortical thickness form naturally occurring morphological subtypes. K-means algorithm clustering was applied to regional cortical thickness values obtained from 256 structural MRI scans (179 patients with schizophrenia and 77 healthy controls [HCs]). GAP statistics revealed three clusters with distinct regional thickness patterns. The specific patterns of cortical thinning, clinical characteristics, and cognitive function of each clustered subgroup were assessed. The three clusters based on thickness patterns comprised of a morphologically impoverished subgroup (25% patients, 1% HCs), an intermediate subgroup (47% patients, 46% HCs), and an intact subgroup (28% patients, 53% HCs). The differences of clinical features among three clusters pertained to age-of-onset, N-back performance, duration exposure to treatment, total burden of positive symptoms, and severity of delusions. Particularly, the morphologically impoverished group had deficits in N-back performance and less severe positive symptom burden. The data-driven neuroimaging approach illustrates the occurrence of morphologically separable subgroups in schizophrenia, with distinct clinical characteristics. We infer that the anatomical heterogeneity of schizophrenia arises from both pathological deviance and physiological variance. We advocate using MRI-guided stratification for clinical trials as well as case-control investigations in schizophrenia.
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PMID:Morphological Profiling of Schizophrenia: Cluster Analysis of MRI-Based Cortical Thickness Data. 3190 40