Computer-Aided Cancer Diagnosis and Grading via Sparse Directional Image Representations

Prostate cancer and breast cancer are the second cause of death among cancers in males and females, respectively. If not diagnosed, prostate and breast cancers can spread and metastasize to other organs and bones and make it impossible for treatment. Hence, early diagnosis of cancer is vital for patient survival. Histopathological evaluation of the tissue is used for cancer diagnosis. The tissue is taken during biopsies and stained using hematoxylin and eosin (H&E) stain. Then a pathologist looks for abnormal changes in the tissue to diagnose and grade the cancer. This process can be time-consuming and subjective. A reliable and repetitive automatic cancer diagnosis method can greatly reduce the time while producing more reliable results. The scope of this dissertation is developing computer vision and machine learning algorithms for automatic cancer diagnosis and grading methods with accuracy acceptable by the expert pathologists. Automatic image classification relies on feature representation methods. In this dissertation we developed methods utilizing sparse directional multiscale transforms - specifically shearlet transform - for medical image analysis. We particularly designed theses computer visions-based algorithms and methods to work with H&E images and MRI images. Traditional signal processing methods (e.g. Fourier transform, wavelet transform, etc.) are not suitable for detecting carcinoma cells due to their lack of directional sensitivity. However, shearlet transform has inherent directional sensitivity and multiscale framework that enables it to detect different edges in the tissue images. We developed techniques for extracting holistic and local texture features from the histological and MRI images using histogram and co-occurrence of shearlet coefficients, respectively. Then we combined these features with the color and morphological features using multiple kernel learning (MKL) algorithm and employed support vector machines (SVM) with MKL to classify the medical images. We further investigated the impact of deep neural networks in representing the medical images for cancer detection. The aforementioned engineered features have a few limitations. They lack generalizability due to being tailored to the specific texture and structure of the tissues. They are time-consuming and expensive and need prepossessing and sometimes it is difficult to extract discriminative features from the images. On the other hand, feature learning techniques use multiple processing layers and learn feature representations directly from the data. To address these issues, we have developed a deep neural network containing multiple layers of convolution, max-pooling, and fully connected layers, trained on the Red, Green, and Blue (RGB) images along with the magnitude and phase of shearlet coefficients. Then we developed a weighted decision fusion deep neural network that assigns weights on the output probabilities and update those weights via backpropagation. The final decision was a weighted sum of the decisions from the RGB, and the magnitude and the phase of shearlet networks. We used the trained networks for classification of benign and malignant H&E images and Gleason grading. Our experimental results show that our proposed methods based on feature engineering and feature learning outperform the state-of-the-art and are even near perfect (100%) for some databases in terms of classification accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC) and hence are promising computer-based methods for cancer diagnosis and grading using images

Feature extraction in image processing and deep learning

This thesis develops theoretical analysis of the approximation properties of neural networks, and algorithms to extract useful features of images in fields of deep learning, quantum energy regression and cancer image analysis. The separate applications are connected by using representation systems in harmonic analysis; we focus on deriving proper representations of data using Gabor transform in this thesis. A novel neural network with proven approximation properties dependent on its size is developed using Gabor system. In quantum energy regression, invariant representation of chemical molecules using electron densities is obtained based on the Gabor transform. Additionally, we dig into pooling functions, the feature extractor in deep neural networks, and develop a novel pooling strategy originated from the maximal function with stability property and stable performance. Anisotropic representation of data using the Shearlet transform is also explored in its ability to detect regions of interests of nuclei in cancer images

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

Automatic Gleason Grading of Prostate Cancer using Shearlet Transform and Multiple Kernel Learning

The Gleason grading system is generally used for histological grading of prostate cancer. In this paper, we first introduce using the Shearlet transform and its coefficients as texture features for automatic Gleason grading. The Shearlet transform is a mathematical tool defined based on affine systems and can analyze signals at various orientations and scales and detect singularities, such as image edges. These properties make the Shearlet transform more suitable for Gleason grading compared to the other transform-based feature extraction methods, such as Fourier transform, wavelet transform, etc. We also extract color channel histograms and morphological features. These features are the essential building blocks of what pathologists consider when they perform Gleason grading. Then, we use the multiple kernel learning (MKL) algorithm for fusing all three different types of extracted features. We use support vector machines (SVM) equipped with MKL for the classification of prostate slides with different Gleason grades. Using the proposed method, we achieved high classification accuracy in a dataset containing 100 prostate cancer sample images of Gleason Grades 2–5

Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning

The Gleason grading system is generally used for histological grading of prostate cancer. In this paper, we first introduce using the Shearlet transform and its coefficients as texture features for automatic Gleason grading. The Shearlet transform is a mathematical tool defined based on affine systems and can analyze signals at various orientations and scales and detect singularities, such as image edges. These properties make the Shearlet transform more suitable for Gleason grading compared to the other transform-based feature extraction methods, such as Fourier transform, wavelet transform, etc. We also extract color channel histograms and morphological features. These features are the essential building blocks of what pathologists consider when they perform Gleason grading. Then, we use the multiple kernel learning (MKL) algorithm for fusing all three different types of extracted features. We use support vector machines (SVM) equipped with MKL for the classification of prostate slides with different Gleason grades. Using the proposed method, we achieved high classification accuracy in a dataset containing 100 prostate cancer sample images of Gleason Grades 2–5

A lifestyle intervention to improve outcomes in men with castrate-resistant prostate cancer

Background There is increasing evidence demonstrating that lifestyle interventions of exercise and diet may represent a useful supportive therapy for men with prostate cancer, improving physiological and psychosocial outcomes. There has been limited investigation of the effects of such interventions in men with castrate resistant prostate cancer (CRPC), the terminal phase of the disease. It is not clear how exercise has been implemented in the prostate cancer care pathway and what a successfully implemented exercise programme might look like. Furthermore, the specific treatment and disease related barriers men with CRPC might face engaging in exercise is not documented, particularly when considering their advanced stage of disease. This work described in this thesis covers an exploration of the feasibility and acceptability of an exercise and dietary intervention to improve outcomes in men with CRPC. Methods A healthcare professional survey was conducted to assess the extent to which NHS trusts are meeting the NICE guidelines (CG175, 1.4.19) for exercise training for men with prostate cancer on androgen deprivation therapy (ADT). Semi-structured interviews of UK healthcare professionals, specialising in prostate cancer care and based in UK National Health service (NHS) trusts were conducted. These explored underlying reasons behind the variability in NHS trusts in delivering exercise training programmes and probed the views of the HCPs regarding exercise training, including the acceptability of concurrent use of an anabolic agent for men with CRPC. A feasibility randomised controlled trial (RCT) of an exercise and dietary intervention in CRPC patients was conducted (COMRADE). Men with CRPC recruited to the RCT were randomised on a 1:1 ratio to either the intervention or usual care for 16 weeks. Men allocated the intervention received up to 24 three sessions of supervised resistance exercise a week; supplemented with whey protein and creatine monohydrate; and given dietary advice. They were also asked to partake in at least one independent moderate intensity aerobic activity lasting at least 30 minutes a week. Following the RCT, post study participant focus groups addressed patients’ views on aspects of the study, particularly with regards to acceptability of trial procedures, barriers and facilitators to exercise training and the impact of living with CRPC. Results The healthcare professional survey demonstrated significant variability between NHS trusts in the UK in delivering the NICE guidelines and that a supervised exercise training programme is not currently embedded within "usual care" for prostate cancer. The healthcare professional interviews (n=12) demonstrated support for an individualised and adaptable exercise programme for men with CRPC which could improve fitness and mitigate some of the long term effects of their cancer/cancer therapy. Their opinions reflected that comorbidities and disease/treatment specific barriers to exercise must be taken into account to support better adherence. In the feasibility RCT, n=31 men were recruited from a total of n=3607 screened (recruitment rate=13.6%). There were eighteen in the intervention and thirteen randomised to the control group. The attrition rate was 16%, with n=4 dropping out of the intervention and n=1 death in the control. Adherence to the supervised and independent exercise sessions was 69% and 78% respectively. The adherence to the whey protein was 68% and creatine was 71%. There were 4 AEs associated with trial procedures, none of which were serious. Three primary themes were identified from the participant focus groups (n=3); these included 1) living with CRPC, 2) experience and opinions of the trial, 3) attitudes and experiences of exercise training and physical activity. The findings demonstrated that the study procedures were well received by 25 the participants, including the trial assessments and format of the intervention. Valuable insights were gained for implementing future exercise intervention studies - providing participant perspectives for the success of a lifestyle behaviour study such as COMRADE. Conclusions The findings suggest that it is feasible to randomise and retain men with CRPC to an exercise and diet intervention, however there was a high rate of attrition in the study, due to the complex nature of the disease in these men. Further work is required to address the barriers related to implementation of exercise in the prostate cancer pathway for men with CRPC