Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology

Until recently, Computer-Aided Medical Interventions (CAMI) and Medical Robotics have focused on rigid and non deformable anatomical structures. Nowadays, special attention is paid to soft tissues, raising complex issues due to their mobility and deformation. Mini-invasive digestive surgery was probably one of the first fields where soft tissues were handled through the development of simulators, tracking of anatomical structures and specific assistance robots. However, other clinical domains, for instance urology, are concerned. Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU, radiofrequency, or cryoablation), increasingly early detection of cancer, and use of interventional and diagnostic imaging modalities, recently opened new challenges to the urologist and scientists involved in CAMI. This resulted in the last five years in a very significant increase of research and developments of computer-aided urology systems. In this paper, we propose a description of the main problems related to computer-aided diagnostic and therapy of soft tissues and give a survey of the different types of assistance offered to the urologist: robotization, image fusion, surgical navigation. Both research projects and operational industrial systems are discussed

Prostate Biopsy Assistance System with Gland Deformation Estimation for Enhanced Precision

Computer-assisted prostate biopsies became a very active research area during the last years. Prostate tracking makes it possi- ble to overcome several drawbacks of the current standard transrectal ultrasound (TRUS) biopsy procedure, namely the insufficient targeting accuracy which may lead to a biopsy distribution of poor quality, the very approximate knowledge about the actual location of the sampled tissues which makes it difficult to implement focal therapy strategies based on biopsy results, and finally the difficulty to precisely reach non-ultrasound (US) targets stemming from different modalities, statistical atlases or previous biopsy series. The prostate tracking systems presented so far are limited to rigid transformation tracking. However, the gland can get considerably deformed during the intervention because of US probe pres- sure and patient movements. We propose to use 3D US combined with image-based elastic registration to estimate these deformations. A fast elastic registration algorithm that copes with the frequently occurring US shadows is presented. A patient cohort study was performed, which yielded a statistically significant in-vivo accuracy of 0.83+-0.54mm.Comment: This version of the paper integrates a correction concerning the local similarity measure w.r.t. the proceedings (this typing error could not be corrected before editing the proceedings

Prostate biopsy tracking with deformation estimation

Transrectal biopsies under 2D ultrasound (US) control are the current clinical standard for prostate cancer diagnosis. The isoechogenic nature of prostate carcinoma makes it necessary to sample the gland systematically, resulting in a low sensitivity. Also, it is difficult for the clinician to follow the sampling protocol accurately under 2D US control and the exact anatomical location of the biopsy cores is unknown after the intervention. Tracking systems for prostate biopsies make it possible to generate biopsy distribution maps for intra- and post-interventional quality control and 3D visualisation of histological results for diagnosis and treatment planning. They can also guide the clinician toward non-ultrasound targets. In this paper, a volume-swept 3D US based tracking system for fast and accurate estimation of prostate tissue motion is proposed. The entirely image-based system solves the patient motion problem with an a priori model of rectal probe kinematics. Prostate deformations are estimated with elastic registration to maximize accuracy. The system is robust with only 17 registration failures out of 786 (2%) biopsy volumes acquired from 47 patients during biopsy sessions. Accuracy was evaluated to 0.76$\pm$0.52mm using manually segmented fiducials on 687 registered volumes stemming from 40 patients. A clinical protocol for assisted biopsy acquisition was designed and implemented as a biopsy assistance system, which allows to overcome the draw-backs of the standard biopsy procedure.Comment: Medical Image Analysis (2011) epub ahead of prin

Image-based registration methods for quantification and compensation of prostate motion during trans-rectal ultrasound (TRUS)-guided biopsy

Prostate biopsy is the clinical standard for cancer diagnosis and is typically performed under two-dimensional (2D) transrectal ultrasound (TRUS) for needle guidance. Unfortunately, most early stage prostate cancers are not visible on ultrasound and the procedure suffers from high false negative rates due to the lack of visible targets. Fusion of pre-biopsy MRI to 3D TRUS for targeted biopsy could improve cancer detection rates and volume of tumor sampled. In MRI-TRUS fusion biopsy systems, patient or prostate motion during the procedure causes misalignments in the MR targets mapped to the live 2D TRUS images, limiting the targeting accuracy of the biopsy system. In order to sample smallest clinically significant tumours of 0.5 cm3with 95% confidence, the root mean square (RMS) error of the biopsy system needs to be The target misalignments due to intermittent prostate motion during the procedure can be compensated by registering the live 2D TRUS images acquired during the biopsy procedure to the pre-acquired baseline 3D TRUS image. The registration must be performed both accurately and quickly in order to be useful during the clinical procedure. We developed an intensity-based 2D-3D rigid registration algorithm and validated it by calculating the target registration error (TRE) using manually identified fiducials within the prostate. We discuss two different approaches that can be used to improve the robustness of this registration to meet the clinical requirements. Firstly, we evaluated the impact of intra-procedural 3D TRUS imaging on motion compensation accuracy since the limited anatomical context available in live 2D TRUS images could limit the robustness of the 2D-3D registration. The results indicated that TRE improved when intra-procedural 3D TRUS images were used in registration, with larger improvements in the base and apex regions as compared with the mid-gland region. Secondly, we developed and evaluated a registration algorithm whose optimization is based on learned prostate motion characteristics. Compared to our initial approach, the updated optimization improved the robustness during 2D-3D registration by reducing the number of registrations with a TRE \u3e 5 mm from 9.2% to 1.2% with an overall RMS TRE of 2.3 mm. The methods developed in this work were intended to improve the needle targeting accuracy of 3D TRUS-guided biopsy systems. The successful integration of the techniques into current 3D TRUS-guided systems could improve the overall cancer detection rate during the biopsy and help to achieve earlier diagnosis and fewer repeat biopsy procedures in prostate cancer diagnosis

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)

Toward optimization of target planning for magnetic resonance image-targeted, 3D transrectal ultrasound-guided fusion prostate biopsy

The current clinical standard for diagnosis of prostate cancer (PCa) is 2D transrectal ultrasound (TRUS)-guided biopsy. However, this procedure has a false negative rate of 21-47% and therefore many patients return for repeat biopsies. A potential solution for improving upon this problem is “fusion” biopsy, where magnetic resonance imaging (MRI) is used for PCa detection and localization prior to biopsy. In this procedure, tumours are delineated on pre-procedural MRI and registered to the 3D TRUS needle guidance modality. However, fusion biopsy continues to yield false negative results and there remains a gap in knowledge regarding biopsy needle target selection. Within-tumour needle targets are currently chosen ad hoc by the operating clinician without accounting for guidance system and registration errors. The objective of this thesis was to investigate how the choice of target selection strategy and number of biopsy attempts made per lesion may affect PCa diagnosis in the presence of needle delivery error. A fusion prostate biopsy simulation software platform was developed, which allowed for the investigation of how needle delivery error affects PCa diagnosis and cancer burden estimation. Initial work was conducted using 3D lesions contoured on MRI by collaborating radiologists. The results indicated that more than one core must be taken from the majority of lesions to achieve a sampling probability 95% for a biopsy system with needle delivery error ≥ 3.5 mm. Furthermore, it was observed that the optimal targeting scheme depends on the relative levels of systematic and random needle delivery errors inherent to the specific fusion biopsy system. Lastly, PCa tumours contoured on digital histology images by genitourinary pathologists were used to conduct biopsy simulations. The results demonstrated that needle delivery error has a substantial impact on the biopsy core involvement observed, and that targeting of high-grade lesions may result in higher core involvement variability compared with lesions of all grades. This work represents a first step toward improving the manner in which lesions are targeted using fusion biopsy. Successful integration of these findings into current fusion biopsy system operation could lead to earlier PCa diagnosis with the need for fewer repeat biopsy procedures

Validation Strategies Supporting Clinical Integration of Prostate Segmentation Algorithms for Magnetic Resonance Imaging

Segmentation of the prostate in medical images is useful for prostate cancer diagnosis and therapy guidance. However, manual segmentation of the prostate is laborious and time-consuming, with inter-observer variability. The focus of this thesis was on accuracy, reproducibility and procedure time measurement for prostate segmentation on T2-weighted endorectal magnetic resonance imaging, and assessment of the potential of a computer-assisted segmentation technique to be translated to clinical practice for prostate cancer management. We collected an image data set from prostate cancer patients with manually-delineated prostate borders by one observer on all the images and by two other observers on a subset of images. We used a complementary set of error metrics to measure the different types of observed segmentation errors. We compared expert manual segmentation as well as semi-automatic and automatic segmentation approaches before and after manual editing by expert physicians. We recorded the time needed for user interaction to initialize the semi-automatic algorithm, algorithm execution, and manual editing as necessary. Comparing to manual segmentation, the measured errors for the algorithms compared favourably with observed differences between manual segmentations. The measured average editing times for the computer-assisted segmentation were lower than fully manual segmentation time, and the algorithms reduced the inter-observer variability as compared to manual segmentation. The accuracy of the computer-assisted approaches was near to or within the range of observed variability in manual segmentation. The recorded procedure time for prostate segmentation was reduced using computer-assisted segmentation followed by manual editing, compared to the time required for fully manual segmentation

Medical Image Registration Using Deep Neural Networks

Registration is a fundamental problem in medical image analysis wherein images are transformed spatially to align corresponding anatomical structures in each image. Recently, the development of learning-based methods, which exploit deep neural networks and can outperform classical iterative methods, has received considerable interest from the research community. This interest is due in part to the substantially reduced computational requirements that learning-based methods have during inference, which makes them particularly well-suited to real-time registration applications. Despite these successes, learning-based methods can perform poorly when applied to images from different modalities where intensity characteristics can vary greatly, such as in magnetic resonance and ultrasound imaging. Moreover, registration performance is often demonstrated on well-curated datasets, closely matching the distribution of the training data. This makes it difficult to determine whether demonstrated performance accurately represents the generalization and robustness required for clinical use. This thesis presents learning-based methods which address the aforementioned difficulties by utilizing intuitive point-set-based representations, user interaction and meta-learning-based training strategies. Primarily, this is demonstrated with a focus on the non-rigid registration of 3D magnetic resonance imaging to sparse 2D transrectal ultrasound images to assist in the delivery of targeted prostate biopsies. While conventional systematic prostate biopsy methods can require many samples to be taken to confidently produce a diagnosis, tumor-targeted approaches have shown improved patient, diagnostic, and disease management outcomes with fewer samples. However, the available intraoperative transrectal ultrasound imaging alone is insufficient for accurate targeted guidance. As such, this exemplar application is used to illustrate the effectiveness of sparse, interactively-acquired ultrasound imaging for real-time, interventional registration. The presented methods are found to improve registration accuracy, relative to state-of-the-art, with substantially lower computation time and require a fraction of the data at inference. As a result, these methods are particularly attractive given their potential for real-time registration in interventional applications

Salvage Brachytherapy for Biochemically Recurrent Prostate Cancer Following Primary Brachytherapy

Purpose. In this study, we evaluated our experience with salvage brachytherapy after discovery of biochemical recurrence after a prior brachytherapy procedure. Methods and Materials. From 2001 through 2012 twenty-one patients treated by brachytherapy within University of Kentucky or from outside centers developed biochemical failure and had no evidence of metastases. Computed tomography (CT) scans were evaluated; patients who had an underseeded portion of their prostate were considered for reimplantation. Results. The majority of the patients in this study (61.9%) were low risk and median presalvage PSA was 3.49 (range 17.41–1.68). Mean follow-up was 61 months. At last follow-up after reseeding, 11/21 (52.4%) were free of biochemical recurrence. There was a trend towards decreased freedom from biochemical recurrence in low risk patients (p = 0.12). International Prostate Symptom Scores (IPSS) increased at 3-month follow-up visits but decreased and were equivalent to baseline scores at 18 months. Conclusions. Salvage brachytherapy after primary brachytherapy is possible; however, in our experience the side-effect profile after the second brachytherapy procedure was higher than after the first brachytherapy procedure. In this cohort of patients we demonstrate that approximately 50% oncologic control, low risk patients appear to have better outcomes than others