Fully Automatic Segmentation of Lumbar Vertebrae from CT Images using Cascaded 3D Fully Convolutional Networks

We present a method to address the challenging problem of segmentation of lumbar vertebrae from CT images acquired with varying fields of view. Our method is based on cascaded 3D Fully Convolutional Networks (FCNs) consisting of a localization FCN and a segmentation FCN. More specifically, in the first step we train a regression 3D FCN (we call it "LocalizationNet") to find the bounding box of the lumbar region. After that, a 3D U-net like FCN (we call it "SegmentationNet") is then developed, which after training, can perform a pixel-wise multi-class segmentation to map a cropped lumber region volumetric data to its volume-wise labels. Evaluated on publicly available datasets, our method achieved an average Dice coefficient of 95.77 $\pm$ 0.81% and an average symmetric surface distance of 0.37 $\pm$ 0.06 mm.Comment: 5 pages and 5 figure

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

Automatic Generation and Novel Validation of Patient-Specific, Anatomically Inclusive Scoliosis Models for Biomechanics-Informed Surgical Planning

Scoliosis is an abnormal spinal curvature of greater than 10 degrees. Severe scoliotic deformities are addressed with highly invasive procedures: anterior or posterior spinal fusion approaches. This invasiveness is due, in part, to the constraints of current surgical planning, which utilizes computed tomography (CT) scans unable to discern spinal ligaments that are dissected to make the spine sufficiently compliant for correction. If localization of ligaments and soft tissues were achieved pre-operatively, corrective procedures could become safer and more efficient by using finite element (FE) biomechanical simulations to determine decreased incidences of ligament releases. This research aims to achieve ligament localization within CT scans by deforming computer-aided design (CAD) meshes that encompass vertebrae, intervertebral discs, ligaments, and other soft tissues to emulate patient-specific anatomy. Models are generated through deformable surface algorithms that elastically fit CAD meshes onto segmentations of conspicuous structures. Surrounding soft tissues are locally warped to reconstruct contextually appropriate positions before the CAD mesh is tetrahedralized to support finite element studies. The methods presented use convolutional neural networks (CNNs) that segment vertebrae from CT images to improve initial deformation alignment. In instances of CNN failure, methodological robustness, given an accurate segmentation, is demonstrated through the use of spinal columns which have been molded into a Lenke classification. Dice coefficient and Hausdorff distance metrics demonstrate the accuracy of the deformable model generation. Synthetically generated images are used for additional validation of soft tissue positioning. Quantitative results are highly competitive and qualitative interpretations suggest a strong level of accuracy and appropriate deformation. Soft tissue ground truths, present in synthetic data, provide further confirmation of accurate mesh generation. Following the completion of the methodological pipeline, accurate, patient-specific, anatomically inclusive models are ready for use in FE studies.https://digitalcommons.odu.edu/gradposters2021_engineering/1005/thumbnail.jp

A multi-center milestone study of clinical vertebral CT segmentation

A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention

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

A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI

We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs. This involves using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies and convolutional image-to-image translation followed by beam search to label vertebral levels in a self-consistent manner. The method can be applied without modification to lumbar, cervical and thoracic-only scans across a range of different MR sequences. The resulting system achieves 98.1% detection rate and 96.5% identification rate on a challenging clinical dataset of whole spine scans and matches or exceeds the performance of previous systems on lumbar-only scans. Finally, we demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.Comment: Accepted full paper to Medical Image Computing and Computer Assisted Intervention 2020. 11 pages plus appendi