One of the most important problems in the segmentation of lung nodules in CT imaging arises from possible attachments occurring between nodules and other lung structures, such as vessels or pleura. In this report, we address the problem of vessels attachments by proposing an automated correction method applied to an initial rough segmentation of the lung nodule. The method is based on a local shape analysis of the initial segmentation making use of 3-D geodesic distance map representations. The correction method has the advantage that it locally refines the nodule segmentation along recognized vessel attachments only, without modifying the nodule boundary elsewhere. The method was tested using a simple initial rough segmentation, obtained by a fixed image thresholding. The validation of the complete segmentation algorithm was carried out on small lung nodules, identified in the ITALUNG screening trial and on small nodules of the lung image database consortium (LIDC) dataset. In fully automated mode, 217/256 (84.8%) lung nodules of ITALUNG and 139/157 (88.5%) individual marks of lung nodules of LIDC were correctly outlined and an excellent reproducibility was also observed. By using an additional interactive mode, based on a controlled manual interaction, 233/256 (91.0%) lung nodules of ITALUNG and 144/157 (91.7%) individual marks of lung nodules of LIDC were overall correctly segmented. The proposed correction method could also be usefully applied to any existent nodule segmentation algorithm for improving the segmentation quality of juxta-vascular nodules.

Oral NO production increases during de novo deposition of dental plaque. NO might be an early host defence mechanism against bacterial proliferation in the plaque. Such a mechanism is inhibited by cigarette smoking.

Nodule growth as observed in computed tomography (CT) scans acquired at different times is the primary feature to malignancy of indeterminate small lung nodules. In this paper, we propose the estimation of nodule size through a scale-space representation which needs no segmentation and has high intra- and inter-operator reproducibility. Lung nodules usually appear in CT images as blob-like patterns and can be analyzed in the scale-space by Laplacian of Gaussian ( LoG ) kernels. For each nodular pattern the LoG scale-space signature was computed and the related characteristic scale adopted as measurement of nodule size. Both in vitro and in vivo validation of LoG characteristic scale were carried out. In vitro validation was done by 40 nondeformable phantoms and 10 deformable phantoms. A close relationship between the characteristic scale and the equivalent diameter, i.e., the diameter of the sphere having the same volume of nodules, (Pearson correlation coefficient was 0.99) and, for nodules undergoing little deformations (obtained at constant volume), small variability of the characteristic scale was observed. The in vivo validation was performed on low and standard-dose CT scans collected from the ITALUNG screening trial (86 nodules) and from the LIDC public data set (89 solid nodules and 40 part-solid nodules or ground-glass opacities). The Pearson correlation coefficient between characteristic scale and equivalent diameter was 0.83-0.93 for ITALUNG and 0.68-0.83 for LIDC data set. Intra- and inter-operator reproducibility of characteristic scale was excellent: on a set of 40 lung nodules of ITALUNG data, two radiologists produced identical results in repeated measurements. The scan-rescan variability of the characteristic scale was also investigated on 86 two-year-stable solid lung nodules (each one observed, on average, in four CT scans) identified in the ITALUNG screening trial: a coefficient of repeatability of about 0.9 mm was observed. Experimental evidence supports the clinical use of the LoG characteristic scale to measure nodule size in CT imaging.

Monitoring devices enable control of the correct execution of a given task during functional magnetic resonance imaging (fMRI) acquisitions and analysis of behavioral features that can influence brain activation patterns In this report, we describe and validate a low-cost device for monitoring hand tracing and writing tasks during fMRI The subject holds a light-emitting pen whose light spot is recorded by a fixed camera aligned with the tracing plane Pen trajectories are extracted by a blob detection algorithm through Laplacian of Gaussian filtering applied to the camera recordings Following phantom and in vivo experiments which demonstrated MR compatibility, the device was applied to monitor the particular case of the task of continuous and self-paced writing of an '8' figure in 10 healthy subjects They underwent fMRI examinations during the task under three conditions spontaneous frequency and figure size, 'low' frequency and 'small' figure size The task recordings were analyzed with a dedicated algorithm that computed both frequency and area of the figures '8' writing The device was judged comfortable by all subjects fMRI data analysis showed that task frequency influenced the activation within primary sensory motor and premotor frontal cortices, while figure size interfered with the activation in posterior parietal cortex Both frequency and size parameters modulated activation in the inferior cerebellum By monitoring writing-tasks executions, this device is expected to broaden the spectrum of applications of fMRI Indeed, it could allow the investigation of patients suffering from neurological disorders affecting handwriting, such as apraxic disorders, cerebellar disorders, or parkinsonism

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