METHODS: This was a retrospective study utilizing records of patients referred to a tertiary urogynecological service between November 2012 and March 2013. Patients underwent a standardized interview, clinical assessment using the POP quantification system of the International Continence Society and four-dimensional translabial ultrasound. The craniocaudal difference in the location of minimal distances in mid-sagittal and coronal planes was determined by offline analysis of ultrasound volumes, and provided a numerical measure of warping. We tested potential predictors, such as demographic factors, signs and symptoms of prolapse, levator avulsion and levator distensibility, for an association with warping.
RESULTS: Full datasets were available for 190 women. The mean craniocaudal difference in location of minimal distances in mid-sagittal and coronal planes was -1.26 mm (range, -6.7 to 4.6 mm; P
METHODS: In this study, we propose a multi-view secondary input residual (MV-SIR) convolutional neural network model for 3D lung nodule segmentation using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset of chest computed tomography (CT) images. Lung nodule cubes are prepared from the sample CT images. Further, from the axial, coronal, and sagittal perspectives, multi-view patches are generated with randomly selected voxels in the lung nodule cubes as centers. Our model consists of six submodels, which enable learning of 3D lung nodules sliced into three views of features; each submodel extracts voxel heterogeneity and shape heterogeneity features. We convert the segmentation of 3D lung nodules into voxel classification by inputting the multi-view patches into the model and determine whether the voxel points belong to the nodule. The structure of the secondary input residual submodel comprises a residual block followed by a secondary input module. We integrate the six submodels to classify whether voxel points belong to nodules, and then reconstruct the segmentation image.
RESULTS: The results of tests conducted using our model and comparison with other existing CNN models indicate that the MV-SIR model achieves excellent results in the 3D segmentation of pulmonary nodules, with a Dice coefficient of 0.926 and an average surface distance of 0.072.
CONCLUSION: our MV-SIR model can accurately perform 3D segmentation of lung nodules with the same segmentation accuracy as the U-net model.
Results: 87 articles were screened to get an update on the desired information. 74 were excluded based on a complete screening, and finally, 13 articles were recruited for complete reviewing. Discussion. The MFP is subjected to stress, which is reflected in the form of compressive and tensile strengths. The stress is mainly concentrated the resection line and around the apices of roots of teeth next to the defect. Diversity of designs and techniques were introduced to optimize the stress distribution, such as modification of the clasp design, using materials with different mechanical properties for dentures base and retainer, use of dental (DI) and/or zygomatic implants (ZI), and free flap reconstruction before prosthetic rehabilitation.
Conclusion: Using ZI in the defective side of the dentulous maxillary defect and defective and nondefective side of the edentulous maxillary defect was found more advantageous, in terms of compression and tensile stress and retention, when compared with DI and free flap reconstruction.
METHODS: A Total of 51 patients with 105 carotid artery plaques were screened using 3D and 2D US probes attached to the same US scanner. Two independent observers characterized the plaques based on the morphological features namely echotexture, echogenicity and surface characteristics. The scores assigned to each morphological feature were used to determine intra- and inter-observer performance. The level of agreement was measured using Kappa coefficient.
RESULTS: The first observer with 2D US showed fair (k=0.4-0.59) and very strong (k>0.8) with 3D US intra-observer agreements using three morphological features. The second observer indicated moderate strong (k=0.6-0.79) with 2D US and very strong with 3D US (k>0.8) intra-observer performances. Moderate strong (k=0.6-0.79) and very strong (k>0.8) inter-observer agreements were reported with 2D US and 3D US respectively. The results with 2D and 3D US were correlated 62% using only echotexture and 56% using surface morphology coupled with echogenicity. 3D US gave a lower score than 2D 71% of the time (p=0.005) in disagreement cases.
CONCLUSION: High reproducibility in carotid plaque characterization was obtained using 3D US rather than 2D US. Hence, it can be a preferred imaging modality in routine or follow up plaque screening of patients with carotid artery disease.