Brain tumors with symptomatic onset in the first two years of life

Eighty children who in the first 2 years of life had signs and symptoms relating to a cerebral neoplasm were studied over an 18-year period (1970-1987), the mean follow-up being 8.2 years. In each case age at onset, clinical presentation, tumor location and pathological diagnosis, extent of surgical resection, postoperative mortality, adjuvant therapy length of survival and quality of life were assessed. Supratentorial tumors (59%) were more common than infratentorial. The most frequent clinical presenting feature (70%) was increased intracranial pressure. Sixty-three patients (79%) were operated on and in all of these cases a histological diagnosis was obtained. Astrocytomas (41%) and medulloblastomas (20%) were the most common oncotypes. Surgical mortality was 17.4% and the 5-year survival rate was 54%. Quality of life was assessed for all long-term surviving patients using a specifically designed protocol. Normal physical and intellectual performances were found in 46% of cases, and all together 75% of the patients had sufficient autonomy in daily life. The prognosis is more closely related to tumor location and type of treatment than to histological diagnosis or age at onset.     

Pose estimation of known objects during transmission tomographic image reconstruction

We address the problem of image formation in transmission tomography when metal objects of known composition and shape, but unknown pose, are present in the scan subject. Using an alternating minimization (AM) algorithm, derived from a model in which the detected data are viewed as Poisson-distributed photon counts, we seek to eliminate the streaking artifacts commonly seen in filtered back projection images containing high-contrast objects. We show that this algorithm, which minimizes the I-divergence (or equivalently, maximizes the log-likelihood) between the measured data and model-based estimates of the means of the data, converges much faster when knowledge of the high-density materials (such as brachytherapy applicators or prosthetic implants) is exploited. The algorithm incorporates a steepest descent-based method to find the position and orientation (collectively called the pose) of the known objects. This pose is then used to constrain the image pixels to their known attenuation values, or, for example, to form a mask on the "missing" projection data in the shadow of the objects. Results from two-dimensional simulations are shown in this paper. The extension of the model and methods used to three dimensions is outlined.     

Impact of Lean Production on Perceived Job Autonomy and Job Satisfaction: An Experimental Study

Previous studies have indicated positive and negative effects of lean production on employees’ perceived work characteristics and job attitudes. The most detrimental consequence of lean production is a decrease in the perceived job autonomy of workshop employees. To reduce these negative consequences, we propose human resource practices for integration with lean production. Drawing on the job characteristics model, we hypothesized that the implementation of lean production combined with human resource practices would enhance perceived job autonomy, job satisfaction, and operational performance. To evaluate our hypotheses, we used an experimental design consisting of a simulation game that mimics a manufacturing company. We implemented lean production combined with human resource practices in this simulated company. The results indicated a significant increase in perceived job autonomy, job satisfaction, and operational performance. Moreover, the results revealed a positive relationship between job satisfaction and operational performance.     

Automated Pulmonary Fibrosis Segmentation Using a 3D Multi-Scale Convolutional Encoder-Decoder Approach in Thoracic CT for the Rhesus Macaque with Radiation-Induced Lung Damage

To develop an automated pulmonary fibrosis (PF) segmentation methodology using a 3D multi-scale convolutional encoder-decoder approach following the robust atlas-based active volume model in thoracic CT for Rhesus Macaques with radiation-induced lung damage. 152 thoracic computed tomography scans of Rhesus Macaques with radiation-induced lung damage were collected. The 3D input data are randomly augmented with the Gaussian blurring when applying the 3D multi-scale convolutional encoder-decoder (3D MSCED) segmentation method.PF in each scan was manually segmented in which 70% scans were used as training data, 20% scans were used as validation data, and 10% scans were used as testing data. The performance of the method is assessed based on a10-fold cross validation method. The workflow of the proposed method has two parts. First, the compromised lung volume with acute radiation-induced PF was segmented using a robust atlas-based active volume model. Next, a 3D multi-scale convolutional encoder-decoder segmentation method was developed which merged the higher spatial information from low-level features with the high-level object knowledge encoded in upper network layers. It included a bottom-up feed-forward convolutional neural network and a top-down learning mask refinement process. The quantitative results of our segmentation method achieved mean Dice score of (0.769, 0.853), mean accuracy of (0.996, 0.999), and mean relative error of (0.302, 0.512) with 95% confidence interval. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance in testing data. This method was extensively validated in NHP datasets. The results demonstrated that the approach is more robust relative to PF than other methods. It is a general framework which can easily be applied to segmentation other lung lesions.