Fully automatic cervical vertebrae segmentation framework for X-ray images

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The cervical spine is a highly flexible anatomy and therefore vulnerable to injuries. Unfortunately, a large number of injuries in lateral cervical X-ray images remain undiagnosed due to human errors. Computer-aided injury detection has the potential to reduce the risk of misdiagnosis. Towards building an automatic injury detection system, in this paper, we propose a deep learning-based fully automatic framework for segmentation of cervical vertebrae in X-ray images. The framework first localizes the spinal region in the image using a deep fully convolutional neural network. Then vertebra centers are localized using a novel deep probabilistic spatial regression network. Finally, a novel shape-aware deep segmentation network is used to segment the vertebrae in the image. The framework can take an X-ray image and produce a vertebrae segmentation result without any manual intervention. Each block of the fully automatic framework has been trained on a set of 124 X-ray images and tested on another 172 images, all collected from real-life hospital emergency rooms. A Dice similarity coefficient of 0.84 and a shape error of 1.69 mm have been achieved

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

Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction

Various deep learning models have been proposed for 3D bone shape reconstruction from two orthogonal (biplanar) X-ray images. However, it is unclear how these models compare against each other since they are evaluated on different anatomy, cohort and (often privately held) datasets. Moreover, the impact of the commonly optimized image-based segmentation metrics such as dice score on the estimation of clinical parameters relevant in 2D-3D bone shape reconstruction is not well known. To move closer toward clinical translation, we propose a benchmarking framework that evaluates tasks relevant to real-world clinical scenarios, including reconstruction of fractured bones, bones with implants, robustness to population shift, and error in estimating clinical parameters. Our open-source platform provides reference implementations of 8 models (many of whose implementations were not publicly available), APIs to easily collect and preprocess 6 public datasets, and the implementation of automatic clinical parameter and landmark extraction methods. We present an extensive evaluation of 8 2D-3D models on equal footing using 6 public datasets comprising images for four different anatomies. Our results show that attention-based methods that capture global spatial relationships tend to perform better across all anatomies and datasets; performance on clinically relevant subgroups may be overestimated without disaggregated reporting; ribs are substantially more difficult to reconstruct compared to femur, hip and spine; and the dice score improvement does not always bring a corresponding improvement in the automatic estimation of clinically relevant parameters.Comment: accepted to NeurIPS 202

Fully automatic image analysis framework for cervical vertebra in X-ray images

Despite the advancement in imaging technologies, a fifth of the injuries in the cervical spine remain unnoticed in the X-ray radiological exam. About a two-third of the subjects with unnoticed injuries suffer tragic consequences. Based on the success of computer-aided systems in several medical image modalities to enhance clinical interpretation, we have proposed a fully automatic image analysis framework for cervical vertebrae in X-ray images. The framework takes an X-ray image as input and highlights different vertebral features at the output. To the best of our knowledge, this is the first fully automatic system in the literature for the analysis of the cervical vertebrae. The complete framework has been built by cascading specialized modules, each of which addresses a specific computer vision problem. This dissertation explores data-driven supervised machine learning solutions to these problems. Given an input X-ray image, the first module localizes the spinal region. The second module predicts vertebral centers from the spinal region which are then used to generate vertebral image patches. These patches are then passed through machine learning modules that detect vertebral corners, highlight vertebral boundaries, segment vertebral body and predict vertebral shapes. In the process of building the complete framework, we have proposed and compared different solutions to the problems addressed by each of the modules. A novel region-aware dense classification deep neural network has been proposed for the first module to address the spine localization problem. The proposed network outperformed the standard dense classification network and random forestbased methods. Location of the vertebral centers and corners vary based on human interpretation and thus are better represented by probability maps than single points. To learn the mapping between the vertebral image patches and the probability maps, a novel neural network capable of predicting a spatially distributed probabilistic distribution has been proposed. The network achieved expert-level performance in localizing vertebral centers and outperform the Harris corner detector and Hough forest-based methods for corner localization. The proposed network has also shown its capability for detecting vertebral boundaries and produced visually better results than the dense classification network-based boundary detectors. Segmentation of the vertebral body is a crucial part of the proposed framework. A new shapeaware loss function has been proposed for training a segmentation network to encourage prediction of vertebra-like structures. The segmentation performance improved significantly, however, the pixel-wise nature of proposed loss function was not able to constrain the predictions adequately. To solve the problem a novel neural network was proposed which predicts vertebral shapes and trains on a loss function defined in the shape space. The proposed shape predictor network was capable of learning better topological information about the vertebra than the shape-aware segmentation network. The methods proposed in this dissertation have been trained and tested on a challenging dataset of X-ray images collected from medical emergency rooms. The proposed, first-of-its-kind, fully automatic framework produces state-of-the-art results both quantitatively and qualitatively

Deformable Multisurface Segmentation of the Spine for Orthopedic Surgery Planning and Simulation

Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data. Approach: This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. 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: Results demonstrate validation against user-assisted expert segmentation, showing excellent boundary agreement and prevention of spatial overlap between neighboring surfaces. This section also plots the characteristics of the statistical shape model, such as compactness, generalizability and specificity, as a function of the number of modes used to represent the family of shapes. Final results demonstrate a proof-of-concept deformation application based on the open-source surgery simulation Simulation Open Framework Architecture toolkit. Conclusions: To summarize, we present a deformable multisurface model that embeds a shape statistics force, with applications to surgery planning and simulation

PadChest: A large chest x-ray image dataset with multi-label annotated reports

We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray database suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/

Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs

An important factor for the development of spinal degeneration, pain and the outcome of spinal surgery is known to be the balance of the spine. It must be analyzed in an upright, standing position to ensure physiological loading conditions and visualize load-dependent deformations. Despite the complex 3D shape of the spine, this analysis is currently performed using 2D radiographs, as all frequently used 3D imaging techniques require the patient to be scanned in a prone position. To overcome this limitation, we propose a deep neural network to reconstruct the 3D spinal pose in an upright standing position, loaded naturally. Specifically, we propose a novel neural network architecture, which takes orthogonal 2D radiographs and infers the spine’s 3D posture using vertebral shape priors. In this work, we define vertebral shape priors using an atlas and a spine shape prior, incorporating both into our proposed network architecture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 0.95, indicating an almost perfect 2D-to-3D domain translation. Validating the reconstruction accuracy of a 3D standing spine on real data is infeasible due to the lack of a valid ground truth. Hence, we design a novel experiment for this purpose, using an orientation invariant distance metric, to evaluate our model’s ability to synthesize full-3D, upright, and patient-specific spine models. We compare the synthesized spine shapes from clinical upright standing radiographs to the same patient’s 3D spinal posture in the prone position from CT

SPNet: Shape Prediction Using a Fully Convolutional Neural Network

This is the author accepted manuscript. The final version is available from Springer via the DOI in this recordShape has widely been used in medical image segmentation algorithms to constrain a segmented region to a class of learned shapes. Recent methods for object segmentation mostly use deep learning algorithms. The state-of-the-art deep segmentation networks are trained with loss functions defined in a pixel-wise manner, which is not suitable for learning topological shape information and constraining segmentation results. In this paper, we propose a novel shape predictor network for object segmentation. The proposed deep fully convolutional neural network learns to predict shapes instead of learning pixel-wise classification. We apply the novel shape predictor network to X-ray images of cervical vertebra where shape is of utmost importance. The proposed network is trained with a novel loss function that computes the error in the shape domain. Experimental results demonstrate the effectiveness of the proposed method to achieve state-of-the-art segmentation, with correct topology and accurate fitting that matches expert segmentation.Engineering and Physical Sciences Research Council (EPSRC)Royal Devon and Exeter NHS Foundation Trus

Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis

Driven by the large foundation models, the development of artificial intelligence has witnessed tremendous progress lately, leading to a surge of general interest from the public. In this study, we aim to assess the performance of OpenAI's newest model, GPT-4V(ision), specifically in the realm of multimodal medical diagnosis. Our evaluation encompasses 17 human body systems, including Central Nervous System, Head and Neck, Cardiac, Chest, Hematology, Hepatobiliary, Gastrointestinal, Urogenital, Gynecology, Obstetrics, Breast, Musculoskeletal, Spine, Vascular, Oncology, Trauma, Pediatrics, with images taken from 8 modalities used in daily clinic routine, e.g., X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Digital Subtraction Angiography (DSA), Mammography, Ultrasound, and Pathology. We probe the GPT-4V's ability on multiple clinical tasks with or without patent history provided, including imaging modality and anatomy recognition, disease diagnosis, report generation, disease localisation. Our observation shows that, while GPT-4V demonstrates proficiency in distinguishing between medical image modalities and anatomy, it faces significant challenges in disease diagnosis and generating comprehensive reports. These findings underscore that while large multimodal models have made significant advancements in computer vision and natural language processing, it remains far from being used to effectively support real-world medical applications and clinical decision-making. All images used in this report can be found in https://github.com/chaoyi-wu/GPT-4V_Medical_Evaluation

Reliability of Robotic Ultrasound Scanning for Scoliosis Assessment in Comparison with Manual Scanning

Background: Ultrasound (US) imaging for scoliosis assessment is challenging for a non-experienced operator. The robotic scanning was developed to follow a spinal curvature with deep learning and apply consistent forces to the patient' back. Methods: 23 scoliosis patients were scanned with US devices both, robotically and manually. Two human raters measured each subject's spinous process angles (SPA) on robotic and manual coronal images. Results: The robotic method showed high intra- (ICC > 0.85) and inter-rater (ICC > 0.77) reliabilities. Compared with the manual method, the robotic approach showed no significant difference (p < 0.05) when measuring coronal deformity angles. The MAD for intra-rater analysis lies within an acceptable range from 0 deg to 5 deg for a minimum of 86% and a maximum 97% of a total number of the measured angles. Conclusions: This study demonstrated that scoliosis deformity angles measured on ultrasound images obtained with robotic scanning are comparable to those obtained by manual scanning