Detection of Bone Tumours in Radiographic Images using Neural Networks

Large Intraosseous Lipoma of the Skull: A Case Report and Review of the Literature

BACKGROUND

Intraosseous lipomas occurring within the skull are rare. Currently, the known locations include the frontal bone, parietal bone, temporal bone, and ethmoid bone. Thus far, we have found only 12 cases of lipoma at the top of the forehead on the skull, and only 2 cases of recurrent intraosseous lipoma have been reported. The patient with intraosseous lipoma we have described was a 3-year-old boy, the youngest of the 12 known patients with intraosseous lipoma of the skull, and the patient had repeated relapses.

CASE DESCRIPTION

We report the history of a 3-year-old boy with a giant intraosseous lipoma in his left frontal region when he came to our hospital for treatment. The head computed tomography scan showed a left frontal plate with an oval low density, and the boundary was clear. The lesion affected the normal appearance of the child. He had experienced varying degrees of recurrent growth after 2 operations. At the last follow-up examination, the appearance of the patient had improved greatly.

CONCLUSIONS

For large intraosseous lipomas occurring in the skull, owing to its special position and the tendency for repeated relapses to develop, extending the excision is an effective surgical approach. For swelling involving a wide range, surgery should involve specialists in craniofacial surgery.

Hair Segmentation Using Heuristically-Trained Neural Networks

We present a method for binary classification using neural networks (NNs) that performs training and classification on the same data using the help of a pretraining heuristic classifier. The heuristic classifier is initially used to segment data into three clusters of high-confidence positives, high-confidence negatives, and low-confidence sets. The high-confidence sets are used to train an NN, which is then used to classify the low-confidence set. Applying this method to the binary classification of hair versus nonhair patches, we obtain a 2.2% performance increase using the heuristically trained NN over the current state-of-the-art hair segmentation method.

A neural network approach for image reconstruction in electron magnetic resonance tomography

An object-oriented, artificial neural network (ANN) based, application system for reconstruction of two-dimensional spatial images in electron magnetic resonance (EMR) tomography is presented. The standard back propagation algorithm is utilized to train a three-layer sigmoidal feed-forward, supervised, ANN to perform the image reconstruction. The network learns the relationship between the 'ideal' images that are reconstructed using filtered back projection (FBP) technique and the corresponding projection data (sinograms). The input layer of the network is provided with a training set that contains projection data from various phantoms as well as in vivo objects, acquired from an EMR imager. Twenty five different network configurations are investigated to test the ability of the generalization of the network. The trained ANN then reconstructs two-dimensional temporal spatial images that present the distribution of free radicals in biological systems. Image reconstruction by the trained neural network shows better time complexity than the conventional iterative reconstruction algorithms such as multiplicative algebraic reconstruction technique (MART). The network is further explored for image reconstruction from 'noisy' EMR data and the results show better performance than the FBP method. The network is also tested for its ability to reconstruct from limited-angle EMR data set.

Intraosseous lipoma: retrospective analysis of 29 patients

We reviewed 29 patients with intraosseous lipoma treated between 1985 and 2002. Mean patient age was 48 (20-75) years. According to Milgram's classification, ten cases were classified as stage I, 14 as stage II, and three as stage III. All patients were initially treated by curettage. In 11 cases an additional phenolization was performed. The average follow-up was 32 (6-208) months. At the last follow-up, none had any clinical or radiological signs of recurrence. The adequate treatment of a symptomatic intraosseous lipoma is curettage and bone grafting. In the current study, phenolization showed no added benefit. An asymptomatic intraosseous lipoma without impending pathological fracture can be treated conservatively.

Detection of areas with viable remnant tumor in postchemotherapy patients with Ewing's sarcoma by dynamic contrast-enhanced MRI using pharmacokinetic modeling

An approach is presented for monitoring the effects of neoadjuvant chemotherapy in patients with Ewing's sarcoma using dynamic contrast-enhanced perfusion magnetic resonance (MR) images. For that purpose, we modify the three-compartment pharmacokinetic permeability model introduced by Tofts et al. (Magn Reson Med 1991;17:357-67) to a two-compartment model. Perfusion MR images acquired using an intravenous injection with Gadolinium (Gd-DTPA) are analyzed with this two-compartment pharmacokinetic model as well as the with an extended pharmacokinetic model that includes the (local) arrival time t(0) of the tracer as an endogenous (estimated) parameter. For each MR section, a wash-in parameter associated with each voxel is estimated twice by fitting each of the two pharmacokinetic models to the dynamic MR signal. A comparison of the two wash-in parametric images (global versus local arrival time) with matched histologic macroslices demonstrates a good correspondence between areas with viable remnant tumor and a high wash-in rate. This can be explained by the high number and permeability of the (leaking) capillaries in viable tumor tissue. The novel pharmacokinetic model based on a local arrival time of tracer results in the best fit of the wash-in rate, the most important factor discerning viable from nonviable tumor components. However, parameter estimates obtained with this model are also more sensitive to noise in the MR signal. The novel pharmacokinetic model resulted in a sensitivity between 0.22 and 0.60 and a specificity between 0.61 and 1. The model based on a global arrival time gave sensitivities between 0.33 and 0.77 and specificities between 0.58 and 0.99. Both statistics are computed as the fraction of correctly labeled voxels (viable or nonviable tumor) within a specified ROI, which delineates the tumor. We conclude that the added value of estimating the local arrival time of tracer first manifests itself for moderate noise levels in the MR signal. The novel pharmacokinetic model should moreover be preferred when pharmacokinetic modeling is applied on the average signal intensity within a ROI, where noise has less effect on the fitted parameters.