A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Establishing density-dependent longitudinal sound speed in the vertebral lamina

Focused ultrasound treatments of the spinal cord may be facilitated using a phased array transducer and beamforming to correct spine-induced focal aberrations. Simulations can non-invasively calculate aberration corrections using x-ray computed tomography (CT) data that are correlated to density ( ρ) and longitudinal sound speed ( cL). We aimed to optimize vertebral lamina-specific [Formula: see text] functions at a physiological temperature (37 °C) to maximize time domain simulation accuracy. Odd-numbered ex vivo human thoracic vertebrae were imaged with a clinical CT-scanner (0.511 × 0.511 × 0.5 mm), then sonicated with a transducer (514 kHz) focused on the canal via the vertebral lamina. Vertebra-induced signal time shifts were extracted from pressure waveforms recorded within the canals. Measurements were repeated 5× per vertebra, with 2.5 mm vertical vertebra shifts between measurements. Linear functions relating cL with CT-derived density were optimized. The optimized function was [Formula: see text] m/s, where w denotes water, giving the tested laminae a mean bulk density of 1600 ± 30 kg/m3 and a mean bulk cL of 1670 ± 60 m/s. The optimized lamina [Formula: see text] function was accurate to [Formula: see text] when implemented in a multi-layered ray acoustics model. This modelling accuracy will improve trans-spine ultrasound beamforming

The state-of-the-art in ultrasound-guided spine interventions.

During the last two decades, intra-operative ultrasound (iUS) imaging has been employed for various surgical procedures of the spine, including spinal fusion and needle injections. Accurate and efficient registration of pre-operative computed tomography or magnetic resonance images with iUS images are key elements in the success of iUS-based spine navigation. While widely investigated in research, iUS-based spine navigation has not yet been established in the clinic. This is due to several factors including the lack of a standard methodology for the assessment of accuracy, robustness, reliability, and usability of the registration method. To address these issues, we present a systematic review of the state-of-the-art techniques for iUS-guided registration in spinal image-guided surgery (IGS). The review follows a new taxonomy based on the four steps involved in the surgical workflow that include pre-processing, registration initialization, estimation of the required patient to image transformation, and a visualization process. We provide a detailed analysis of the measurements in terms of accuracy, robustness, reliability, and usability that need to be met during the evaluation of a spinal IGS framework. Although this review is focused on spinal navigation, we expect similar evaluation criteria to be relevant for other IGS applications

Multimodal image registration of the scoliotic torso for surgical planning

Background This paper presents a method that registers MRIs acquired in prone position, with surface topography (TP) and X-ray reconstructions acquired in standing position, in order to obtain a 3D representation of a human torso incorporating the external surface, bone structures, and soft tissues. Methods TP and X-ray data are registered using landmarks. Bone structures are used to register each MRI slice using an articulated model, and the soft tissue is confined to the volume delimited by the trunk and bone surfaces using a constrained thin-plate spline. Results The method is tested on 3 pre-surgical patients with scoliosis and shows a significant improvement, qualitatively and using the Dice similarity coefficient, in fitting the MRI into the standing patient model when compared to rigid and articulated model registration. The determinant of the Jacobian of the registration deformation shows higher variations in the deformation in areas closer to the surface of the torso. Conclusions The novel, resulting 3D full torso model can provide a more complete representation of patient geometry to be incorporated in surgical simulators under development that aim at predicting the effect of scoliosis surgery on the external appearance of the patient’s torso.Canadian Institute for Health and Research (CIHR

Multi-Surface Simplex Spine Segmentation for Spine Surgery Simulation and Planning

This research proposes to develop a knowledge-based multi-surface simplex deformable model for segmentation of healthy as well as pathological lumbar spine data. It aims to provide a more accurate and robust segmentation scheme for identification of intervertebral disc pathologies to assist with spine surgery planning. A robust technique that combines multi-surface and shape statistics-aware variants of the deformable simplex model is presented. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user-assistance is allowed to disable the prior shape influence during deformation. Results have been validated against user-assisted expert segmentation

Ultrasound-based navigated pedicle screw insertion without intraoperative radiation: feasibility study on porcine cadavers

BACKGROUND Navigation systems for spinal fusion surgery rely on intraoperative computed tomography (CT) or fluoroscopy imaging. Both expose patient, surgeons and operating room staff to significant amounts of radiation. Alternative methods involving intraoperative ultrasound (iUS) imaging have recently shown promise for image-to-patient registration. Yet, the feasibility and safety of iUS navigation in spinal fusion have not been demonstrated. PURPOSE To evaluate the accuracy of pedicle screw insertion in lumbar and thoracolumbar spinal fusion using a fully automated iUS navigation system. STUDY DESIGN Prospective porcine cadaver study. METHODS Five porcine cadavers were used to instrument the lumbar and thoracolumbar spine using posterior open surgery. During the procedure, iUS images were acquired and used to establish automatic registration between the anatomy and preoperative CT images. Navigation was performed with the preoperative CT using tracked instruments. The accuracy of the system was measured as the distance of manually collected points to the preoperative CT vertebral surface and compared against fiducial-based registration. A postoperative CT was acquired, and screw placements were manually verified. We report breach rates, as well as axial and sagittal screw deviations. RESULTS A total of 56 screws were inserted (5.50 mm diameter n=50, and 6.50 mm diameter n=6). Fifty-two screws were inserted safely without breach. Four screws (7.14%) presented a medial breach with an average deviation of 1.35±0.37 mm (all <2 mm). Two breaches were caused by 6.50 mm diameter screws, and two by 5.50 mm screws. For vertebrae instrumented with 5.50 mm screws, the average axial diameter of the pedicle was 9.29 mm leaving a 1.89 mm margin in the left and right pedicle. For vertebrae instrumented with 6.50 mm screws, the average axial diameter of the pedicle was 8.99 mm leaving a 1.24 mm error margin in the left and right pedicle. The average distance to the vertebral surface was 0.96 mm using iUS registration and 0.97 mm using fiducial-based registration. CONCLUSIONS We successfully implanted all pedicle screws in the thoracolumbar spine using the ultrasound-based navigation system. All breaches recorded were minor (<2 mm) and the breach rate (7.14%) was comparable to existing literature. More investigation is needed to evaluate consistency, reproducibility, and performance in surgical context. CLINICAL SIGNIFICANCE Intraoperative US-based navigation is feasible and practical for pedicle screw insertion in a porcine model. It might be used as a low-cost and radiation-free alternative to intraoperative CT and fluoroscopy in the future

Toward real-time rigid registration of intra-operative ultrasound with preoperative CT images for lumbar spinal fusion surgery

Purpose Accurate and effective registration of the vertebrae is crucial for spine surgical navigation procedures. Patient movement, surgical instrumentation or inadvertent contact with the tracked reference during the intervention may invalidate the registration, requiring a rapid correction of the misalignment. In this paper, we present a framework to rigidly align preoperative computed tomography (CT) with the intra-operative ultrasound (iUS) images of a single vertebra. Methods We use a single caudo-cranial axial sweep procedure to acquire iUS images, from which the scan trajectory is exploited to initialize the registration transform. To refine the transform, locations of the posterior vertebra surface are first extracted, then used to compute the CT-to-iUS image intensity gradient-based alignment. The approach was validated on a lumbosacral section of a porcine cadaver. Results We achieved an overall median accuracy of 1.48 mm (success rate of 84.42%) in ~11 s of computation time, satisfying the clinically accepted accuracy threshold of 2 mm. Conclusion Our approach using intra-operative ultrasound to register patient vertebral anatomy to preoperative images matches the clinical needs in terms of accuracy and computation time, facilitating its integration into the surgical workflow

Evaluation of an Ultrasound-Based Navigation System for Spine Neurosurgery: A Porcine Cadaver Study

During the last two decades, intra-operative ultrasound (iUS) imaging has been employed for various surgical procedures of the spine, including spinal fusion and needle injections. Accurate and efficient registration of pre-operative computed tomography or magnetic resonance images with iUS images are key elements in the success of iUS-based spine navigation. While widely investigated in research, iUS-based spine navigation has not yet been established in the clinic. This is due to several factors including the lack of a standard methodology for the assessment of accuracy, robustness, reliability, and usability of the registration method. To address these issues, we present a systematic review of the state-of-the-art techniques for iUS-guided registration in spinal image-guided surgery (IGS). The review follows a new taxonomy based on the four steps involved in the surgical workflow that include pre-processing, registration initialization, estimation of the required patient to image transformation, and a visualization process. We provide a detailed analysis of the measurements in terms of accuracy, robustness, reliability, and usability that need to be met during the evaluation of a spinal IGS framework. Although this review is focused on spinal navigation, we expect similar evaluation criteria to be relevant for other IGS applications