Multimodal T2w and DWI Prostate Gland Automated Registration

Multiparametric magnetic resonance imaging (mpMRI) is emerging as a promising tool in the clinical pathway of prostate cancer (PCa). The registration between a structural and a functional imaging modality, such as T2-weighted (T2w) and diffusion-weighted imaging (DWI) is fundamental in the development of a mpMRI-based computer aided diagnosis (CAD) system for PCa. Here, we propose an automated method to register the prostate gland in T2w and DWI image sequences by a landmark-based affine registration and a non-parametric diffeomorphic registration. An expert operator manually segmented the prostate gland in both modalities on a dataset of 20 patients. Target registration error and Jaccard index, which measures the overlap between masks, were evaluated pre-and post-registration resulting in an improvement of 44% and 21%, respectively. In the future, the proposed method could be useful in the framework of a CAD system for PCa detection and characterization in mpMRI

Cross-Modality Image Registration using a Training-Time Privileged Third Modality

— In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered. As an example, we focus on aligning intra-subject multiparametric Magnetic Resonance (mpMR) images, between T2-weighted (T2w) scans and diffusionweighted scans with high b-value (DWI_{high−b}). For the application of localising tumours in mpMR images, diffusion scans with zero b-value (DWI_{b=0}) are considered easier to register to T2w due to the availability of corresponding features. We propose a learning from privileged modality algorithm, using a training-only imaging modality DWIb=0, to support the challenging multi-modality registration problems. We present experimental results based on 369 sets of 3D multiparametric MRI images from 356 prostate cancer patients and report, with statistical significance, a lowered median target registration error of 4.34 mm, when registering the holdout DWI_{high−b} and T2w image pairs, compared with that of 7.96 mm before registration. Results also show that the proposed learning-based registration networks enabled efficient registration with comparable or better accuracy, compared with a classical iterative algorithm and other tested learning-based methods with/without the additional modality. These compared algorithms also failed to produce any significantly improved alignment between DWI_{high−b} and T2w in this challenging application

Radiomics in prostate cancer: an up-to-date review

: Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications

Comparison of Histogram-based Textural Features between Cancerous and Normal Prostatic Tissue in Multiparametric Magnetic Resonance Images

In the last decade, multiparametric magnetic resonance imaging (mpMRI) has been expanding its role in prostate cancer detection and characterization. In this work, 19 patients with clinically significant peripheral zone (PZ) tumours were studied. Tumour masks annotated on the whole-mount histology sections were mapped on T2-weighted (T2w) and diffusion-weighted (DW) sequences. Gray-level histograms of tumoral and normal tissue were compared using six first-order texture features. Multivariate analysis of variance (MANOVA) was used to compare group means. Mean intensity signal of ADC showed the highest showed the highest area under the receiver operator characteristics curve (AUC) equal to 0.85. MANOVA analysis revealed that ADC features allows a better separation between normal and cancerous tissue with respect to T2w features (ADC: P = 0.0003, AUC = 0.86; T2w: P = 0.03, AUC = 0.74). MANOVA proved that the combination of T2-weighted and apparent diffusion coefficient (ADC) map features increased the AUC to 0.88. Histogram-based features extracted from invivo mpMRI can help discriminating significant PZ PCa

Deep learning for an improved diagnostic pathway of prostate cancer in a small multi-parametric magnetic resonance data regime

Prostate Cancer (PCa) is the second most commonly diagnosed cancer among men, with an estimated incidence of 1.3 million new cases worldwide in 2018. The current diagnostic pathway of PCa relies on prostate-specific antigen (PSA) levels in serum. Nevertheless, PSA testing comes at the cost of under-detection of malignant lesions and a substantial over-diagnosis of indolent ones, leading to unnecessary invasive testing such biopsies and treatment in indolent PCa lesions. Magnetic Resonance Imaging (MRI) is a non-invasive technique that has emerged as a valuable tool for PCa detection, staging, early screening, treatment planning and intervention. However, analysis of MRI relies on expertise, can be time-consuming, requires specialized training and in its absence suffers from inter and intra-reader variability and sub-optimal interpretations. Deep Learning (DL) techniques have the ability to recognize complex patterns in imaging data and are able to automatize certain assessments or tasks while offering a lesser degree of subjectiveness, providing a tool that can help clinicians in their daily tasks. In spite of it, DL success has traditionally relied on the availability of large amounts of labelled data, which are rarely available in the medical field and are costly and hard to obtain due to privacy regulations of patients’ data and required specialized training, among others. This work investigates DL algorithms specially tailored to work in a limited data regime with the final objective of improving the current prostate cancer diagnostic pathway by improving the performance of DL algorithms for PCa MRI applications in a limited data regime scenario. In particular, this thesis starts by exploring Generative Adversarial Networks (GAN) to generate synthetic samples and their effect on tasks such as prostate capsule segmentation and PCa lesion significance classification (triage). Following, we explore the use of Auto-encoders (AEs) to exploit the data imbalance that is usually present in medical imaging datasets. Specifically, we propose a framework based on AEs to detect the presence of prostate lesions (tumours) by uniquely learning from control (healthy) data in an outlier detection-like fashion. This thesis also explores more recent DL paradigms that have shown promising results in natural images: generative and contrastive self-supervised learning (SSL). In both cases, we propose specific prostate MRI image manipulations for a PCa lesion classification downstream task and show the improvements offered by the techniques when compared with other initialization methods such as ImageNet pre-training. Finally, we explore data fusion techniques in order to leverage different data sources in the form of MRI sequences (orthogonal views) acquired by default during patient examinations and that are commonly ignored in DL systems. We show improvements in a PCa lesion significance classification when compared to a single input system (axial view)

Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature

Prostate cancer (PCa) represents the fourth most common cancer and the fifth leading cause of cancer death of men worldwide. Multiparametric MRI (mp-MRI) has high sensitivity and specificity in the detection of PCa, and it is currently the most widely used imaging technique for tumor localization and cancer staging. mp-MRI plays a key role in risk stratification of naive patients, in active surveillance for low-risk patients, and in monitoring recurrence after definitive therapy. Radiomics is an emerging and promising tool which allows a quantitative tumor evaluation from radiological images via conversion of digital images into mineable high-dimensional data. The purpose of radiomics is to increase the features available to detect PCa, to avoid unnecessary biopsies, to define tumor aggressiveness, and to monitor post-treatment recurrence of PCa. The integration of radiomics data, including different imaging modalities (such as PET-CT) and other clinical and histopathological data, could improve the prediction of tumor aggressiveness as well as guide clinical decisions and patient management. The purpose of this review is to describe the current research applications of radiomics in PCa on MR images