23 research outputs found
Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks
This paper presents a method for automatic segmentation, localization, and
identification of vertebrae in arbitrary 3D CT images. Many previous works do
not perform the three tasks simultaneously even though requiring a priori
knowledge of which part of the anatomy is visible in the 3D CT images. Our
method tackles all these tasks in a single multi-stage framework without any
assumptions. In the first stage, we train a 3D Fully Convolutional Networks to
find the bounding boxes of the cervical, thoracic, and lumbar vertebrae. In the
second stage, we train an iterative 3D Fully Convolutional Networks to segment
individual vertebrae in the bounding box. The input to the second networks have
an auxiliary channel in addition to the 3D CT images. Given the segmented
vertebra regions in the auxiliary channel, the networks output the next
vertebra. The proposed method is evaluated in terms of segmentation,
localization, and identification accuracy with two public datasets of 15 3D CT
images from the MICCAI CSI 2014 workshop challenge and 302 3D CT images with
various pathologies introduced in [1]. Our method achieved a mean Dice score of
96%, a mean localization error of 8.3 mm, and a mean identification rate of
84%. In summary, our method achieved better performance than all existing works
in all the three metrics
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
IMAGE ANALYSIS FOR SPINE SURGERY: DATA-DRIVEN DETECTION OF SPINE INSTRUMENTATION & AUTOMATIC ANALYSIS OF GLOBAL SPINAL ALIGNMENT
Spine surgery is a therapeutic modality for treatment of spine disorders, including spinal deformity, degeneration, and trauma. Such procedures benefit from accurate localization of surgical targets, precise delivery of instrumentation, and reliable validation of surgical objectives – for example, confirming that the surgical implants are delivered as planned and desired changes to the global spinal alignment (GSA) are achieved. Recent advances in surgical navigation have helped to improve the accuracy and precision of spine surgery, including intraoperative imaging integrated with real-time tracking and surgical robotics. This thesis aims to develop two methods for improved image-guided surgery using image analytic techniques. The first provides a means for automatic detection of pedicle screws in intraoperative radiographs – for example, to streamline intraoperative assessment of implant placement. The algorithm achieves a precision and recall of 0.89 and 0.91, respectively, with localization accuracy within ~10 mm. The second develops two algorithms for automatic assessment of GSA in computed tomography (CT) or cone-beam CT (CBCT) images, providing a means to quantify changes in spinal curvature and reduce the variability in GSA measurement associated with manual methods. The algorithms demonstrate GSA estimates with 93.8% of measurements within a 95% confidence interval of manually defined truth. Such methods support the goals of safe, effective spine surgery and provide a means for more quantitative intraoperative quality assurance. In turn, the ability to quantitatively assess instrument placement and changes in GSA could represent important elements of retrospective analysis of large image datasets, improved clinical decision support, and improved patient outcomes