Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

International audienceProstate cancer is the second most diagnosed cancer of men all over the world. In the last decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed improving diagnosis.In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systemshave been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field ofresearch for the last ten years. This survey aims to provide a comprehensive review of the state of the art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aidedsystem. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to theresearch community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey

Segmentation of pelvic structures from preoperative images for surgical planning and guidance

Prostate cancer is one of the most frequently diagnosed malignancies globally and the second leading cause of cancer-related mortality in males in the developed world. In recent decades, many techniques have been proposed for prostate cancer diagnosis and treatment. With the development of imaging technologies such as CT and MRI, image-guided procedures have become increasingly important as a means to improve clinical outcomes. Analysis of the preoperative images and construction of 3D models prior to treatment would help doctors to better localize and visualize the structures of interest, plan the procedure, diagnose disease and guide the surgery or therapy. This requires efficient and robust medical image analysis and segmentation technologies to be developed. The thesis mainly focuses on the development of segmentation techniques in pelvic MRI for image-guided robotic-assisted laparoscopic radical prostatectomy and external-beam radiation therapy. A fully automated multi-atlas framework is proposed for bony pelvis segmentation in MRI, using the guidance of MRI AE-SDM. With the guidance of the AE-SDM, a multi-atlas segmentation algorithm is used to delineate the bony pelvis in a new \ac{MRI} where there is no CT available. The proposed technique outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. With the SDM of pelvis and its segmented surface, an accurate 3D pelvimetry system is designed and implemented to measure a comprehensive set of pelvic geometric parameters for the examination of the relationship between these parameters and the difficulty of robotic-assisted laparoscopic radical prostatectomy. This system can be used in both manual and automated manner with a user-friendly interface. A fully automated and robust multi-atlas based segmentation has also been developed to delineate the prostate in diagnostic MR scans, which have large variation in both intensity and shape of prostate. Two image analysis techniques are proposed, including patch-based label fusion with local appearance-specific atlases and multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images when limited labeled atlases are available. The proposed techniques can achieve more robust and accurate segmentation results than other multi-atlas based methods. The seminal vesicles are also an interesting structure for therapy planning, particularly for external-beam radiation therapy. As existing methods fail for the very onerous task of segmenting the seminal vesicles, a multi-atlas learning framework via random decision forests with graph cuts refinement has further been proposed to solve this difficult problem. Motivated by the performance of this technique, I further extend the multi-atlas learning to segment the prostate fully automatically using multispectral (T1 and T2-weighted) MR images via hybrid \ac{RF} classifiers and a multi-image graph cuts technique. The proposed method compares favorably to the previously proposed multi-atlas based prostate segmentation. The work in this thesis covers different techniques for pelvic image segmentation in MRI. These techniques have been continually developed and refined, and their application to different specific problems shows ever more promising results.Open Acces

Automatic Cancer Tissue Detection Using Multispectral Photoacoustic Imaging

Convolutional neural networks (CNNs) have become increasingly popular in recent years because of their ability to tackle complex learning problems such as object detection, and object localization. They are being used for a variety of tasks, such as tissue abnormalities detection and localization, with an accuracy that comes close to the level of human predictive performance in medical imaging. The success is primarily due to the ability of CNNs to extract the discriminant features at multiple levels of abstraction. Photoacoustic (PA) imaging is a promising new modality that is gaining significant clinical potential. The availability of a large dataset of three dimensional PA images of ex-vivo human prostate and thyroid specimens has facilitated this current study aimed at evaluating the efficacy of CNN for cancer diagnosis. In PA imaging, a short pulse of near-infrared laser light is sent into the tissue, but the image is created by focusing the ultrasound waves that are photoacoustically generated due to the absorption of light, thereby mapping the optical absorption in the tissue. By choosing multiple wavelengths of laser light, multispectral photoacoustic (MPA) images of the same tissue specimen can be obtained. The objective of this thesis is to implement deep learning architecture for cancer detection using the MPA image dataset. In this study, we built and examined a fully automated deep learning framework that learns to detect and localize cancer regions in a given specimen entirely from its MPA image dataset. The dataset for this work consisted of samples with size ranging from 12 × 45 × 200 pixels to 64 × 64 × 200 pixels at five wavelengths namely, 760 nm, 800 nm, 850 nm, 930 nm, and 970 nm. The proposed algorithms first extract features using convolutional kernels and then detect cancer tissue using the softmax function, the last layer of the network. The AUC was calculated to evaluate the performance of the cancer tissue detector with a very promising result. To the best of our knowledge, this is one of the first examples of the application of deep 3D CNN to a large cancer MPA dataset for the prostate and thyroid cancer detection. While previous efforts using the same dataset involved decision making using mathematically extracted image features, this work demonstrates that this process can be automated without any significant loss in accuracy. Another major contribution of this work has been to demonstrate that both prostate and thyroid datasets can be combined to produce improved results for cancer diagnosis

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Comprehensive Framework for Computer-Aided Prostate Cancer Detection in Multi-Parametric MRI

Prostate cancer is the most diagnosed form of cancer and one of the leading causes of cancer death in men, but survival rates are relatively high with sufficiently early diagnosis. The current clinical model for initial prostate cancer screening is invasive and subject to overdiagnosis. As such, the use of magnetic resonance imaging (MRI) has recently grown in popularity as a non-invasive imaging-based prostate cancer screening method. In particular, the use of high volume quantitative radiomic features extracted from multi-parametric MRI is gaining attraction for the auto-detection of prostate tumours since it provides a plethora of mineable data which can be used for both detection and prognosis of prostate cancer. Current image-based cancer detection methods, however, face notable challenges that include noise in MR images, variability between different MRI modalities, weak contrast, and non-homogeneous texture patterns, making it difficult for diagnosticians to identify tumour candidates. In this thesis, a comprehensive framework for computer-aided prostate cancer detection using multi-parametric MRI was introduced. The framework consists of two parts: i) a saliency-based method for identifying suspicious regions in multi-parametric MR prostate images based on statistical texture distinctiveness, and ii) automatic prostate tumour candidate detection using a radiomics-driven conditional random field (RD-CRF). The framework was evaluated using real clinical prostate multi-parametric MRI data from 20 patients, and both parts were compared against state-of-the-art approaches. The suspicious region detection method achieved a 1.5% increase in sensitivity, and a 10% increase in specificity and accuracy over the state-of-the-art method, indicating its potential for more visually meaningful identification of suspicious tumour regions. The RD-CRF method was shown to improve the detection of tumour candidates by mitigating sparsely distributed tumour candidates and improving the detected tumour candidates via spatial consistency and radiomic feature relationships. Thus, the developed framework shows potential for aiding medical professionals with performing more efficient and accurate computer-aided prostate cancer detection

Multi-Modality Diffuse Fluorescence Imaging Applied to Preclinical Imaging in Mice

RÉSUMÉ Cette thèse vise à explorer l'information anatomique et fonctionnelle en développant de nouveaux systèmes d'imagerie de fluorescence macroscopiques à base de multi-modalité. L‘ajout de l‘imagerie anatomique à des modalités fonctionnelles telles que la fluorescence permet une meilleure visualisation et la récupération quantitative des images de fluorescence, ce qui en retour permet d'améliorer le suivi et l'évaluation des paramètres biologiques dans les tissus. Sur la base de cette motivation, la fluorescence a été combinée avec l‘imagerie ultrasonore (US) d'abord et ensuite l'imagerie par résonance magnétique (IRM). Dans les deux cas, les performances du système ont été caractérisées et la reconstruction a été évaluée par des simulations et des expérimentations sur des fantômes. Finalement, ils ont été utilisés pour des expériences d'imagerie moléculaire in vivo dans des modèles de cancer et d‘athérosclérose chez la souris. Les résultats ont été présentés dans trois articles, qui sont inclus dans cette thèse et décrits brièvement ci-dessous. Un premier article présente un système d'imagerie bimodalité combinant fluorescence à onde continue avec l‘imagerie à trois dimensions (3D) US. A l‘aide de stages X-Y motorisés, le système d'imagerie a été en mesure de recueillir l‘émission fluorescente et les échos acoustiques délimitant la surface 3D et la position des inclusions fluorescentes dans l'échantillon. Une validation sur fantômes, a montré que l'utilisation des priors anatomiques provenant des US améliorait la qualité de la reconstruction fluorescente. En outre, un étude pilote in-vivo en utilisant une souris Apo-E a évalué la faisabilité de cette approche d'imagerie double modalité pour de futures études pré-cliniques. Dans un deuxième effort, et sur la base du premier travail, nous avons amélioré le système d'imagerie par fluorescence-US au niveau des algorithmes, de la précision----------ABSTRACT This thesis aims to explore the anatomical and functional information by developing new macroscopic multi-modality fluorescence imaging schemes. Adding anatomical imaging to functional modalities such as fluorescence enables better visualization and recovery of fluorescence images, in turn, improving the monitoring and assessment of biological parameters in tissue. Based on this motivation, fluorescence was combined with ultrasound (US) imaging first and then magnetic resonance imaging (MRI). In both cases, the systems characterization and reconstruction performance were evaluated by simulations and phantom experiments. Eventually, they were applied to in vivo molecular imaging in models of cancer and atherosclerosis in mice. Results were presented in three peer-reviewed journals, which are included in this thesis and shortly described below. A first article presented a dual-modality imaging system combining continuous-wave transmission fluorescence imaging with three dimensional (3D) US imaging. Using motorized X-Y stages, the fluorescence-US imaging system was able to collect boundary fluorescent emission, and acoustic pulse-echoes delineating the 3D surface and position of fluorescent inclusions within the sample. A validation in phantoms showed that using the US anatomical priors, the fluorescent reconstruction quality was significantly improved. Furthermore, a pilot in-vivo study using an Apo-E mouse evaluated the feasibility of this dual-modality imaging approach for future animal studies. In a second endeavor, and based on the first work, we improved the fluorescence-US imaging system in terms of sampling precision and reconstruction algorithms. Specifically, now combining US imaging and profilometry, both the fluorescent target and 3D surface of sample could be obtained in order to achieve improved fluorescence reconstruction. Furthermore,

Enhancement of Near-Infrared Diffuse Optical Tomography for Prostate Cancer Imaging

The objective of this study is to enhance the functional imaging modality of Diffuse Optical Tomography (DOT) for the early diagnosis and accurate detection of prostate cancer. Enhancement approaches such as spatial prior extracted from Trans-Rectal Ultrasound (TRUS), spectral prior pre-calibrated with the absorption chromophores in biological tissue and metabolic fluorescence emitting biomarker has been implemented by either instrumentation or computational modeling techniques. Findings and Conclusions: A hard prior based hierarchical reconstruction algorithm for a TRUS-DOT combined system is developed and validated by both simulation and tissue phantom experiments.A Parametric Recovery Uncertainty Level (PRUL) model is derived for DOT reconstruction based on measurements at both single and multiple wavelengths. The model indicates that steady state DOT system could produce reconstruction results comparable to frequency domain systems and direct current measurement components are not redundant in frequency domain reconstruction. The model can also be used to optimize the wavelength combination in multispectral optical imaging systems.A rapid DOT system is fabricated based on a wavelength-swept light source. The system is validated by phantom experiments and is readily extendable for fluorescence signal acquisition.A geometric-differential-sensitivity reconstruction algorithm is developed for reflectance imaging geometry in DOT for accurate target depth recovery.The experiment setup for FDOT validation is designed. Preliminary simulation results support the hypothesis that the reverse fluorohphore uptake can be recovered within our imaging geometry.School of Electrical & Computer Engineerin