Deep learning assisted MRI guided attenuation correction in PET

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPositron emission tomography (PET) is a unique imaging modality that provides physiological and functional details of the tissue at the molecular level. However, the acquired PET images have some limitations such as the attenuation. PET attenuation correction is an essential step to obtain the full potential of PET quantification. With the wide use of hybrid PET/MR scanners, magnetic resonance (MR) images are used to address the problem of PET attenuation correction. The MR images segmentation is one simple and robust approach to create pseudo computed tomography (CT) images, which are used to generate attenuation coefficient maps to correct the PET attenuation. Recently, deep learning has been proposed and used as a promising technique to efficiently perform MR and various medical images segmentation. In this research work, deep learning guided segmentation approaches have been proposed to enhance the bone class segmentation of MR brain images in order to generate accurate pseudo-CT images. The first approach has introduced the combination of handcrafted features with deep learning features to enrich the set of features. Multiresolution analysis techniques, which generate multiscale and multidirectional coefficients of an image such as contourlet and shearlet transforms, are applied and combined with deep convolutional neural network (CNN) features. Different experiments have been conducted to investigate the number of selected coefficients and the insertion location of the handcrafted features. The second approach aims at reducing the segmentation algorithm’s complexity while maintaining the segmentation performance. An attention based convolutional encode-decoder network has been proposed to adaptively recalibrate the deep network features. This attention based network consists of two different squeeze and excitation blocks that excite the features spatially and channel wise. The two blocks are combined sequentially to decrease the number of network’s parameters and reduces the model complexity. The third approach has been focuses on the application of transfer learning from different MR sequences such as T1 weighted (T1-w) and T2 weighted (T2-w) images. A pretrained model with T1-w MR sequences is fine tuned to perform the segmentation of T2-w images. Multiple fine tuning approaches and experiments have been conducted to study the best fine tuning mechanism that is able to build an efficient segmentation model for both T1-w and T2-w segmentation. Clinical datasets of fifty patients with different conditions and diagnosis have been used to carry an objective evaluation to measure the segmentation performance of the results obtained by the three proposed methods. The first and second approaches have been validated with other studies in the literature that applied deep network based segmentation technique to perform MR based attenuation correction for PET images. The proposed methods have shown an enhancement in the bone segmentation with an increase of dice similarity coefficient (DSC) from 0.6179 to 0.6567 using an ensemble of CNNs with an improvement percentage of 6.3%. The proposed excitation-based CNN has decreased the model complexity by decreasing the number of trainable parameters by more than 46% where less computing resources are required to train the model. The proposed hybrid transfer learning method has shown its superiority to build a multi-sequences (T1-w and T2-w) segmentation approach compared to other applied transfer learning methods especially with the bone class where the DSC is increased from 0.3841 to 0.5393. Moreover, the hybrid transfer learning approach requires less computing time than transfer learning using open and conservative fine tuning

Modeling and Simulation of Bio-pathways using Hybrid Functional Petri Nets

The study of biological systems is growing rapidly, and can be considered as an intrinsic task in biological research and a prerequisite for diagnosing diseases and drug development. The integration of biological studies with computer technologies led to a noticeable development in this field with the appearance of many powerful modeling and simulation techniques and tools. The help of computers in biology resulted in deeper knowledge about complex biological systems and biopathways behaviors. Among modeling tools, the Petri Net formalism plays an important role. Petri Net is a powerful computerized and graphical modeling technique originally developed by Carl Adam Petri in 1960 to model discrete event systems. With its various extensions, Petri Nets find applications in many other fields including Biology. The extension known under the name Hybrid Functional Petri Net (HFPN) was developed specifically to model biological systems. Traditionally, biological processes are captured as systems of ordinary differential equations. However, HFPNs offer a much more elegant and versatile approach to represent these processes more accurately. In fact, these nets allow to capture phenomena which are impossible to capture with ordinary differential equations, while being more intuitive to understand and model with. In this work we propose an approach to automatically translate a system of ordinary differential equations representing a biological process into a HFPN. The resulting HFPN not only preserves the semantics of the original model, but is also more humanly readable thanks to the use of a novel technique to connect its components in a smart way. To validate our approach, we implemented it as an extension to the tool Real Time Studio (an integrated environment for modeling, simulation and automatic verification of real-time systems), and compared our simulation results with those obtained by simulating systems of ordinary differential equations on MATLAB

An automated approach to translate a biological process from ODEs into graphical hybrid functional Petri Nets

The study of biological systems is growing rapidly, and can be considered as an intrinsic task in biological research, and a prerequisite for diagnosing diseases and drug development. The integration of biological studies with computer technologies led to noticeable developments in biology with the appearance of many powerful modeling and simulation techniques and tools. The help of computers in biology resulted in deeper knowledge about complex biological systems and biopathways behaviors. Among modeling tools, the Petri Net formalism plays an important role. Petri Net is a powerful computerized and graphical modeling technique originally developed by Carl Adam Petri in 1960 to model discrete event systems. With its various extensions, Petri Nets find applications in many other fields including Biology. The extension known under the name Hybrid Functional Petri Net (HFPN) was developed specifically to model biological systems. Traditionally, biological processes are captured as systems of ordinary differential equations (ODEs). However, HFPNs offer a much more elegant and versatile approach to represent these processes more accurately. In fact, HFPNs allow to capture phenomena which are impossible to capture with ODES, while being more intuitive and easy to understand and model with. In this work we propose an approach to translate a system of ODEs representing a biological process into a HFPN. The resulting HFPN, not only preserves the semantics of the original model, but is also more humanly readable thanks to the use of a novel technique to connect its components in a smart way. To validate our approach, we implemented it as an extension to the tool Real Time Studio (an integrated environment for modeling, simulation and automatic verification of real-time systems), and compared our simulation results with those obtained by simulating systems of ODEs using MATLAB. 1 2017 IEEE.Scopu

Deep learning with multiresolution handcrafted features for brain MRI segmentation

The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo computed tomography (CT) images which are used to achieve positron emission tomography (PET) attenuation correction. One of the main challenges of creating pseudo CT images is the difficulty to obtain an accurate segmentation of the bone tissue in brain MR images. Deep convolutional neural networks (CNNs) have been widely and efficiently applied to perform MR image segmentation. The aim of this work is to propose a segmentation approach that combines multiresolution handcrafted features with CNN-based features to add directional properties and enrich the set of features to perform segmentation. The main objective is to efficiently segment the brain into three tissue classes: bone, soft tissue, and air. The proposed method combines non subsampled Contourlet (NSCT) and non subsampled Shearlet (NSST) coefficients with CNN's features using different mechanisms. The entropy value is calculated to select the most useful coefficients and reduce the input's dimensionality. The segmentation results are evaluated using fifty clinical brain MR and CT images by calculating the precision, recall, dice similarity coefficient (DSC), and Jaccard similarity coefficient (JSC). The results are also compared to other methods reported in the literature. The DSC of the bone class is improved from 0.6179 ± 0.0006 to 0.6416 ± 0.0006. The addition of multiresolution features of NSCT and NSST with CNN's features demonstrates promising results. Moreover, NSST coefficients provide more useful information than NSCT coefficients

Brain MR images segmentation using 3D CNN with features recalibration mechanism for segmented CT generation

The segmentation of MR (magnetic resonance) images is a simple approach to create Pseudo CT images which are useful for many medical imaging analysis applications. One of the main challenges of this process is the bone segmentation of brain MR images. Deep convolutional neural networks (CNNs) have been widely and efficiently applied to perform MR images segmentation. The aim of this work is to propose a novel excitation-based CNN by recalibrating the network features adaptively to enhance the bone segmentation by segmenting the brain MR images into three tissue classes: bone, soft tissue, and air. The proposed method combines two types of features excitation mechanisms namely: (1) spatial squeeze and channel excitation block (cSE) and (2) channel squeeze and spatial excitation block (sSE). The two blocks are combined sequentially and integrated seamlessly into a 3D convolutional encoder decoder network. The novelty of this work emerges in the combination of the two excitation blocks sequentially to improve the segmentation performance and reduce the model complexity. The proposed approach is evaluated through a comparison with computed tomography (CT) images as ground truth and validated with other methods in the literature that applied deep CNN approaches to perform MR image segmentation for PET attenuation correction. Brain MR and CT datasets which consist of 50 patients are used to evaluate the proposed method. The segmentation performance of the three brain classes is evaluated using precision, recall, dice similarity coefficient (DSC), and Jaccard index. The presented method improves the bone tissue segmentation compared to the baseline model and other methods in the literature where the DSC is improved from 0.6278 0.0006 to 0.6437 0.0006 with an improvement percentage of 2.53% for bone class. The proposed excitation-based segmentation network architecture demonstrates promising and competitive results compared with other methods in the literature and reduces the model complexity thanks to the sequential combination of the two excitation blocks

MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed