METHODS: A total of 15 femora were examined with four parameters i.e. maximum length of femur (FeMl), diameter of femoral head (FeHd), transverse diameter of midshaft (FeMd) and condylar breadth (FeCb). Osteometric board and vernier calipers were employed for the conventional method, while CT reconstructed images and Osirix MD software was utilised for the virtual method.
RESULTS: Results exhibited that there were no significant differences in the measurements by conventional and virtual methods. There were also no significant differences in the measurements by the intra or inter-observer error analyses. The intraclass correlation coefficients (ICC) were more than 0.95 by both intra and inter-observer error analyses. Technical error of measurement had displayed values within the acceptable ranges (rTEM <0.08 for intra-observer, <2.25 for inter-observer), and coefficient of reliability (R) indicated small measurement errors (R > 0.95 for intra-observer, R > 0.92 for inter-observer). By parameters, FeMl showed the highest R value (0.99) with the least error in different methods and observers (rTEM = 0.02-0.41%). Bland and Altman plots revealed points scattered close to zero indicating perfect agreement by both virtual and conventional methods. The mean differences for FeMl, FeHd, FeMd and FeCb measurements were 0.01 cm, -0.01 cm, 0.02 cm and 0.01 cm, respectively.
CONCLUSION: This brought to suggest that bone measurement by virtual method was highly accurate and reliable as in the conventional method. It is recommended for implementation in the future anthropological studies especially in countries with limited skeletal collection.
AIM: The aim of the present study was to determine sex of human mandible from morphology, morphometric measurements as well as discriminant function analysis from the CT scan.
MATERIALS AND METHODS: The present retrospective study comprised 79 subjects (48 males, 31 females), with age group between 18 and 74 years, and were obtained from the post mortem computed tomography data in the Hospital Kuala Lumpur. The parameters were divided into three morphologic and nine morphometric parameters, which were measured by using Osirix MD Software 3D Volume Rendering.
RESULTS: The Chi-square test showed that men were significantly association with square-shaped chin (92%), prominent muscle marking (85%) and everted gonial glare, whereas women had pointed chin (84%), less prominent muscle marking (90%) and inverted gonial glare (80%). All parameter measurements showed significantly greater values in males than in females by independent t-test (p< 0.01). By discriminant analysis, the classification accuracy was 78.5%, the sensitivity was 79.2% and the specificity was 77.4%. The discriminant function equation was formulated based on bigonial breath and condylar height, which were the best predictors.
CONCLUSION: In conclusion, the mandible could be distinguished according to the sex. The results of the study can be used for identification of damaged and/or unknown mandible in the Malaysian population.
METHODS: One hundred computed tomography scans of disease-free knees were analyzed. A 3-dimensional reconstructed image of the tibia was generated and aligned to its anatomic axis in the coronal and sagittal planes. The tibia was then rotationally aligned to the tibial plateau (tibial centroid axis) and PTS was measured from best-fit planes on the surface of the proximal tibia and individually for the medial and lateral plateaus. This was then repeated with the tibia rotationally aligned to the ankle (transmalleolar axis).
RESULTS: When rotationally aligned to the tibial plateau, the mean PTS, medial PTS, and lateral PTS were 11.2° ± 3.0 (range, 4.7°-17.7°), 11.3° ± 3.2 (range, 2.7°-19.7°), and 10.9° ± 3.7 (range, 3.5°-19.4°), respectively. When rotationally aligned to the ankle, the mean PTS, medial PTS, and lateral PTS were 11.4° ± 3.0 (range, 5.3°-19.3°), 13.9° ± 3.7 (range, 3.1°-24.4°), and 9.7° ± 3.6 (range, 0.8°-17.7°), respectively.
CONCLUSION: The PTS in the normal Asian knee is on average 11° (mean) with a reference range of 5°-17° (mean ± 2 standard deviation). This has implications to surgery and implant design.
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.