Region growing and fuzzy C-means algorithm segmentation for PET images of head-neck tumours

The aim of this work, performed at Azienda Ospedialiero Universitaria in Modena, is the implementation and validation of autosegmentation methods of head and neck (H&N) tumor PET images. These autosegmentation processes are important mostly to overcome the problems of manual segmentation, performed by radiotherapist physician, regarding the contouring time (that can reach more than two hours) and the intra-observer and inter-observer variability. Fuzzy C-means (FCM) and Region Growing (RG) algorithms were developed in a MATLAB GUI that allows to choice iteratively the different steps necessary for a good segmentation. Pre-processing operations were previously applied to improve image quality: a gaussian filter to remove noise and an opening morphological operation to uniform background. NEMA IEC body phantom, acquired with four hot spheres and two cold spheres, was firstly used to test the two methods in known condition. The accuracy of processes was evaluated considering the volume change between calculated and theoretical volume that is always null within error and reaches the highest value in the case of the smallest sphere because of partial volume effect, generally decreasing as sphere size increases. Afterwards, 16 PET images studies of H&N tumors were used for clinical test of algorithms. The efficiency was estimated using two quantitative coefficients: Dice Similarity Index (DSC) and Average Hausdorff Distance (AHD). Mean DSC and AHD values, obtained mediating on all cases, are within literature threshold (0.6 for DSC and about 16 mm for AHD). Contouring time, required to segment all slices of each case, changes from few seconds in FCM to some minutes in RG, always remaining inferior to manual segmentation time. The results are satisfactory, however, they could be improved increasing the number of patients and testing the variability between more experts. FCM could be also applied to lymphomas to test the efficiency in the segmentation of displaced regions

UNet and MobileNet CNN-based model observers for CT protocol optimization: comparative performance evaluation by means of phantom CT images

Purpose: The aim of this work is the development and characterization of a model observer (MO) based on convolutional neural networks (CNNs), trained to mimic human observers in image evaluation in terms of detection and localization of low-contrast objects in CT scans acquired on a reference phantom. The final goal is automatic image quality evaluation and CT protocol optimization to fulfill the ALARA principle. Approach: Preliminary work was carried out to collect localization confidence ratings of human observers for signal presence/absence from a dataset of 30,000 CT images acquired on a PolyMethyl MethAcrylate phantom containing inserts filled with iodinated contrast media at different concentrations. The collected data were used to generate the labels for the training of the artificial neural networks. We developed and compared two CNN architectures based respectively on Unet and MobileNetV2, specifically adapted to achieve the double tasks of classification and localization. The CNN evaluation was performed by computing the area under localization-ROC curve (LAUC) and accuracy metrics on the test dataset. Results: The mean of absolute percentage error between the LAUC of the human observer and MO was found to be below 5% for the most significative test data subsets. An elevated inter-rater agreement was achieved in terms of S-statistics and other common statistical indices. Conclusions: Very good agreement was measured between the human observer and MO, as well as between the performance of the two algorithms. Therefore, this work is highly supportive of the feasibility of employing CNN-MO combined with a specifically designed phantom for CT protocol optimization programs

Fabrication of a hydrogenated amorphous silicon detector in 3-d geometry and preliminary test on planar prototypes

Hydrogenated amorphous silicon (a-Si:H) can be produced by plasma-enhanced chemical vapor deposition (PECVD) of SiH4 (silane) mixed with hydrogen. The resulting material shows outstanding radiation hardness properties and can be deposited on a wide variety of substrates. Devices employing a-Si:H technologies have been used to detect many different kinds of radiation, namely, minimum ionizing particles (MIPs), X-rays, neutrons, and ions, as well as low-energy protons and alphas. However, the detection of MIPs using planar a-Si:H diodes has proven difficult due to their unsatisfactory S/N ratio arising from a combination of high leakage current, high capacitance, and limited charge collection efficiency (50% at best for a 30 µm planar diode). To overcome these limitations, the 3D-SiAm collaboration proposes employing a 3D detector geometry. The use of vertical electrodes allows for a small collection distance to be maintained while preserving a large detector thickness for charge generation. The depletion voltage in this configuration can be kept below 400 V with a consequent reduction in the leakage current. In this paper, following a detailed description of the fabrication process, the results of the tests performed on the planar p-i-n structures made with ion implantation of the dopants and with carrier selective contacts are illustrated