An Automated Liver Vasculature Segmentation from CT Scans for Hepatic Surgical Planning

Liver vasculature segmentation is a crucial step for liver surgical planning. Segmentation of liver vasculature is an important part of the 3D visualisation of the liver anatomy. The spatial relationship between vessels and other liver structures, like tumors and liver anatomic segments, helps in reducing the surgical treatment risks. However, liver vessels segmentation is a challenging task, that is due to low contrast with neighboring parenchyma, the complex anatomy, the very thin branches and very small vessels. This paper introduces a fully automated framework consist of four steps to segment the vessels inside the liver organ. Firstly, in the preprocessing step, a combination of two filtering techniques are used to extract and enhance vessels inside the liver region, first the vesselness filter is used to extract the vessels structure, and then the anisotropic coherence enhancing diffusion (CED) filter is used to enhance the intensity within the tubular vessels structure. This step is followed by a smart multiple thresholding to extract the initial vasculature segmentation. The liver vasculature structures, including hepatic veins connected to the inferior vena cava and the portal veins, are extracted. Finally, the inferior vena cava is segmented and excluded from the vessels segmentation, as it is not considered as part of the liver vasculature structure. The liver vessel segmentation method is validated on the publically available 3DIRCAD datasets. Dice coefficient (DSC) is used to evaluate the method, the average DSC score achieved a score 68.5%. The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning

Development of a 4D digital phantom for Cone-Beam CT (CBCT) imaging on the Varian On-Board Imager (OBI)

Mitigating effects of respiratory motion during image guided radiotherapy (IGRT) is important especially during thoracic and abdomen scanning protocols such as cone-beam CT (CBCT) imaging. However, the lack of ‘ground-truth’ in validating new algorithms has always been a challenge. The objective of this study is to outline the development of a novel 4D digital phantom for simulation of respiratory motion effects during CBCT image reconstruction based on Varian On-Board Imager (OBI): Half-Fan (HF) operating mode geometry. A set of actual 4D Magnetic Resonance (MR) data was used to develop the digital phantom. Firstly, the MR data sequencewasextendedto mimic a standard CBCT imaging acquisition protocol. Then, the images were segmented into several organs of interest and assigned with respective CT attenuation values. Subsequently, 2D projections of the developed digital phantom were simulated using the Varian OBI geometry. A Poisson noise model was also incorporated to the projection data to realistically simulate quantum noise that is present in an actual clinical environment. Three types of projections were then reconstructed using the standard 3D Feldkamp-Davis-Kress (FDK) algorithm, projections: without noise, with noise, and with noise and reconstructed with an additional Hann filter. As validation, the reconstructed images were compared against a single-frame of the developed phantom; quantitatively, using normalized root mean squared error (NRMSE) and qualitatively, using difference images. The results indicated that the phantom managed to display a consistent trend in modeling the effects of respiratory motion on the reconstructed images. On average, the NRMSE values for all three reconstructed images within the entire field-of-view (FOV) were evaluated to be approximately 29.07±0.22%. Nonetheless, the difference images indicated a large error in areas largely affected by respiratory motion. The NRMSE of a region-of-interest (ROI) near the affected area was evaluated as 51.26% that constitute to a significant +22.19% difference

Evaluation Methodology for Respiratory Signal Extraction from Clinical Cone-Beam CT (CBCT) using Data-Driven Methods

The absence of a ground truth for internal motion in clinical studies has always been a challenge to evaluate developed methods to extract respiratory motion especially during a 60-second cone-beam CT (CBCT) scan in Image-Guided Radiotherapy Treatment (IGRT). The unavailability of a gold standard has led this study to present a methodology to manually track respiratory motion on a clinically acquired CBCT projection data set over a 360° view angle. The tracked signal is then used as a reference to assess the performance of four data-driven methods in respiratory motion extraction, namely: the Amsterdam Shroud (AS), Local Principal Component Analysis (LPCA), Intensity Analysis (IA), and Fourier Transform (FT)-based methods that do not require additional equipment nor protocol to the existing treatment delivery. The assessment using this reference signal includes both quantitative and qualitative analysis. It is found out quantitatively that all four methods managed to extract respiratory signals that are highly correlated with the reference signal, with the LPCA method displaying the highest correlation coefficient value at 0.9108. Furthermore, the normalized root-mean-squared amplitude error of detected peaks and troughs within the signal from the LPCA method is also lowest at 1.6529 % compared to the other methods. This result is further supported by qualitative analysis via visual inspection of each extracted signal plotted with the reference signal on the same axes

Strengthening programming skills among engineering students through experiential learning based robotics project

This study examined the educational effects in strengthening programming skills among university’s undergraduate engineering students via integration of a robotics project and an experiential learning approach. In this study, a robotics project was conducted to close the gap of students’ difficulty in relating the theoretical concepts of programming and real-world problems. Hence, an experiential learning approach using the Kolb model was proposed to investigate the problem. In this project, students were split into groups whereby they were asked to develop codes for controlling the navigation of a wheeled mobile robot. They were responsible for managing their group’s activities, conducting laboratory tests, producing technical reports and preparing a video presentation. The statistical analysis performed on the students’ summative assessments of a programming course revealed a remarkable improvement in their problem-solving skills and ability to provide programming solutions to a real-world problem

Inter-subject registration-based segmentation of thoracic-abdominal organs in 4 dimensional magnetic resonance imaging

4 Dimensional Magnetic Resonance Imaging (4D MRI) is currently gaining attention as an imaging modality which is able to capture inter-cycle variability of respiratory motion. Such information is beneficial for example in radiotherapy planning and delivery. In the latter case, there may be a need for organ segmentation, however 4D MRI are of low contrast, which complicates automated organ segmentation. This paper proposes a multi-subject thoracic-abdominal organ segmentation propagation scheme for 4D MRI. The proposed scheme is registration based, hence different combinations of deformation and similarity measures are used. For deformation we used either just an affine transformation or additionally free form deformation on top of an affine transform. For similarity measure, either the sum of squared intensity differences or normalised mutual information is used. Segmentations from multiple subjects are registered to a target MRI and the average segmentation is found. The result of the method is compared with the ground truth which is generated from a semi-automated segmentation method. The results are quantified using the Jaccard index and Hausdorff distance. The results show that using free form deformation with a sum of squared intensity differences similarity measure produces an acceptable segmentation of the organs with an overall Jaccard index of over 0.5. Hence, the proposed scheme can be used as a basis for automated organ segmentation in 4D MRI

Development of a Particle Filter Framework for Respiratory Motion Correction in Nuclear Medicine Imaging

ABSTRACT This research aims to develop a methodological framework based on a data driven approach known as particle filters, often found in computer vision methods, to correct the effect of respiratory motion on Nuclear Medicine imaging data. Particles filters are a popular class of numerical methods for solving optimal estimation problems and we wish to use their flexibility to make an adaptive framework. In this work we use the particle filter for estimating the deformation of the internal organs of the human torso, represented by X, over a discrete time index k. The particle filter approximates the distribution of the deformation of internal organs by generating many propositions, called particles. The posterior estimate is inferred from an observation Z k of the external torso surface. We demonstrate two preliminary approaches in tracking organ deformation. In the first approach, X k represent a small set of organ surface points. In the second approach, X k represent a set of affine organ registration parameters to a reference time index r. Both approaches are contrasted to a comparable technique using direct mapping to infer X k from the observation Z k . Simulations of both approaches using the XCAT phantom suggest that the particle filter-based approaches, on average performs, better

PET image reconstruction incorporating 3D mean-median sinogram filtering

Positron Emission Tomography (PET) projection data or sinogram contained poor statistics and randomness that produced noisy PET images. In order to improve the PET image, we proposed an implementation of pre-reconstruction sinogram filtering based on 3D mean-median filter. The proposed filter is designed based on three aims; to minimise angular blurring artifacts, to smooth flat region and to preserve the edges in the reconstructed PET image. The performance of the pre-reconstruction sinogram filter prior to three established reconstruction methods namely filtered-backprojection (FBP), Maximum likelihood expectation maximization-Ordered Subset (OSEM) and OSEM with median root prior (OSEM-MRP) is investigated using simulated NCAT phantom PET sinogram as generated by the PET Analytical Simulator (ASIM). The improvement on the quality of the reconstructed images with and without sinogram filtering is assessed according to visual as well as quantitative evaluation based on global signal to noise ratio (SNR), local SNR, contrast to noise ratio (CNR) and edge preservation capability. Further analysis on the achieved improvement is also carried out specific to iterative OSEM and OSEM-MRP reconstruction methods with and without pre-reconstruction filtering in terms of contrast recovery curve (CRC) versus noise trade off, normalised mean square error versus iteration, local CNR versus iteration and lesion detectability. Overall, satisfactory results are obtained from both visual and quantitative evaluations