Automatic analysis of medical images for change detection in prostate cancer

Prostate cancer is the most common cancer and second most common cause of cancer death in men in the UK. However, the patient risk from the cancer can vary considerably, and the widespread use of prostate-specific antigen (PSA) screening has led to over-diagnosis and over-treatment of low-grade tumours. It is therefore important to be able to differentiate high-grade prostate cancer from the slowly- growing, low-grade cancer. Many of these men with low-grade cancer are placed on active surveillance (AS), which involves constant monitoring and intervention for risk reclassification, relying increasingly on magnetic resonance imaging (MRI) to detect disease progression, in addition to TRUS-guided biopsies which are the routine clinical standard method to use. This results in a need for new tools to process these images. For this purpose, it is important to have a good TRUS-MR registration so corresponding anatomy can be located accurately between the two. Automatic segmentation of the prostate gland on both modalities reduces some of the challenges of the registration, such as patient motion, tissue deformation, and the time of the procedure. This thesis focuses on the use of deep learning methods, specifically convolutional neural networks (CNNs), for prostate cancer management. Chapters 4 and 5 investigated the use of CNNs for both TRUS and MRI prostate gland segmentation, and reported high segmentation accuracies for both, Dice Score Coefficients (DSC) of 0.89 for TRUS segmentations and DSCs between 0.84-0.89 for MRI prostate gland segmentation using a range of networks. Chapter 5 also investigated the impact of these segmentation scores on more clinically relevant measures, such as MRI-TRUS registration errors and volume measures, showing that a statistically significant difference in DSCs did not lead to a statistically significant difference in the clinical measures using these segmentations. The potential of these algorithms in commercial and clinical systems are summarised and the use of the MRI prostate gland segmentation in the application of radiological prostate cancer progression prediction for AS patients are investigated and discussed in Chapter 8, which shows statistically significant improvements in accuracy when using spatial priors in the form of prostate segmentations (0.63 ± 0.16 vs. 0.82 ± 0.18 when comparing whole prostate MRI vs. only prostate gland region, respectively)

Radiomics approach to the detection of prostate cancer using multiparametric MRI:a validation study using prostate-cancer-tissue-mimicking phantoms

Over the last few years, a number of studies have quantified the role of radiomics, dynamic contrast enhancement and standard MRI (T2WI + DWI) in detecting prostate cancer; however, the aim of this paper was to assess the advantage of combining radiomics with other multiparametric magnetic resonance imaging (mpMRI) (T2-DWI-DCE) in improving the detection of prostate cancer. This study used 10 prostate-cancer-tissue-mimicking phantoms to obtain preclinical data. We then focused on 46 patients who underwent mpMRI and Transrectal Ultrasound (TRUS) guided biopsy between September 2016 and December 2017. The texture analysis parameters combined with the mpMRI and compared with the histopathology of TRUS biopsy have been assessed statistically by principal component analysis (PCA) and discriminant component analysis (DCA). The prediction model and goodness-of-fit were examined with the Akaike information criterion (AIC) and McFadden pseudo-R-squared. In the PCA, there was a higher separation between cancerous and noncancerous tissue in the preclinical compared with the clinical data. Both AIC and R2 showed an improvement in the model in cancer prediction by adding the radiomics to mpMRI. The discriminant analysis showed an accuracy of cancer prediction of 81% compared with 100% in the pre-clinical phantom data. Combining radiomics with mpMRI showed an improvement in prostate cancer prediction. The ex vivo experiments validated the findings of this study

Improved detection and characterization of obscured central gland tumors of the prostate: texture analysis of non contrast and contrast enhanced MR images for differentiation of benign prostate hyperplasia (BPH) nodules and cancer

OBJECTIVE: The purpose of this study to assess the value of texture analysis (TA) for prostate cancer (PCa) detection on T2 weighted images (T2WI) and dynamic contrast-enhanced images (DCE) by differentiating between the PCa and Benign Prostate Hyperplasia (BPH). MATERIALS & METHODS: This study used 10 retrospective MRI data sets that were acquired from men with confirmed PCa. The prostate region of interest (ROI) was delineated by an expert on MRI data sets using automated prostate capsule segmentation scheme. The statistical significance test was used for feature selection scheme for optimal differentiation of PCa from BPH on MR images. In pre-processing, for T2-WI, Bias correction and all images intensities are standardized to a representative template. For DCE images, Bias correction and all images are registered to time point 1 for that patient. Following pre-processing texture, features from ROI were extracted and analyzed. Texture features that were extracted are: Intensity mean and standard deviation, Sobel (Edge detection), Haralick features, and Gabor features. RESULTS: In T2-WI, statistically significant differences were observed in Haralick features. In DCE images, statistically significant differences were observed in mean intensity, Sobel, Gabor, and Haralick features. CONCLUSION: BPH is better differentiated in DCE images compared to T2-WI. The statically significant features may be combined to build a BPH vs. cancer detection system in future

Detection of Prostate Cancer Using Radial/Axial Scanning of 2D Trans-rectal Ultrasound Images

The search for improvement in the result of segmentation of regions of interest in medical images has continued to be a source of challenge to researchers. Several research efforts have gone in to delineate regions of interest in the prostate gland from Trans-rectal ultrasound (TRUS) 2D-images. In this work, we develop a fast algorithm based on radial/axial scanning of the pixels of the prostate gland image with the goal of detecting hyper-echoic pixels that are bound within the boundaries of the gland TRUS 2D-images. The algorithm implements expert knowledge and utilizes the features extracted from the intensity of the TRUS images, primarily the relative intensity and gradient to delineate region of interest. It employs radial/axial scanning of the image from common seed point automatically selected to detect the region of the gland and subsequently hyper-echoic pixels which indicate suspected cancerous tissue cites. Evaluation of the algorithm performance was done by comparing detection result with that of expert radiologists. The detection algorithm gave an average accuracy of 88.55% and sensitivity of 71.65%

Transrectal ultrasound image processing for brachytherapy applications

In this thesis, we propose a novel algorithm for detecting needles and their corresponding implanted radioactive seed locations in the prostate. The seed localization process is carried out efficiently using separable Gaussian filters in a probabilistic Gibbs random field framework. An approximation of the needle path through the prostate volume is obtained using a polynomial fit. The seeds are then detected and assigned to their corresponding needles by calculating local maxima of the voronoi region around the needle position. In our experiments, we were able to successfully localize over 85% of the implanted seeds. Furthermore, as a regular part of a brachytherapy cancer treatment, patient’s prostate is scanned using a trans-rectal ultrasound probe, its boundary is manually outlined, and its volume is estimated for dosimetry purposes. In this thesis, we also propose a novel semi-automatic segmentation algorithm for prostate boundary detection that requires a reduced amount of radiologist’s input, and thus speeds up the surgical procedure. Saved time can be used to re-scan the prostate during the operation and accordingly adjust the treatment plan. The proposed segmentation algorithm utilizes texture differences between ultrasound images of the prostate tissue and the surrounding tissues. It is carried out in 5 the polar coordinate system and it uses three-dimensional data correlation to improve the smoothness and reliability of the segmentation. Test results show that the boundary segmentation obtained from the algorithm can reduce manual input by the factor of 3, without significantly affecting the accuracy of the segmentation (i.e. semi-automatically estimated prostate volume is within 90% of the original estimate)

Spectral clustering for TRUS images

BACKGROUND: Identifying the location and the volume of the prostate is important for ultrasound-guided prostate brachytherapy. Prostate volume is also important for prostate cancer diagnosis. Manual outlining of the prostate border is able to determine the prostate volume accurately, however, it is time consuming and tedious. Therefore, a number of investigations have been devoted to designing algorithms that are suitable for segmenting the prostate boundary in ultrasound images. The most popular method is the deformable model (snakes), a method that involves designing an energy function and then optimizing this function. The snakes algorithm usually requires either an initial contour or some points on the prostate boundary to be estimated close enough to the original boundary which is considered a drawback to this powerful method. METHODS: The proposed spectral clustering segmentation algorithm is built on a totally different foundation that doesn't involve any function design or optimization. It also doesn't need any contour or any points on the boundary to be estimated. The proposed algorithm depends mainly on graph theory techniques. RESULTS: Spectral clustering is used in this paper for both prostate gland segmentation from the background and internal gland segmentation. The obtained segmented images were compared to the expert radiologist segmented images. The proposed algorithm obtained excellent gland segmentation results with 93% average overlap areas. It is also able to internally segment the gland where the segmentation showed consistency with the cancerous regions identified by the expert radiologist. CONCLUSION: The proposed spectral clustering segmentation algorithm obtained fast excellent estimates that can give rough prostate volume and location as well as internal gland segmentation without any user interaction