Vertebral Column Localization, Labeling, and Segmentation

Alomari, Raja’ S.; Ghosh, S. K.; Koh, Jaehan; Chaudhary, Vipin

doi:10.1007/978-3-319-12508-4_7

Cited by 10 publications

(5 citation statements)

References 88 publications

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“…we investigate the automation of localizing and identifying individual vertebrae from CT scans, which can substantially benefit the daily work of radiologists and many subsequent tasks in spinal image analysis. On one hand, the localization and identification results of individual vertebrae can be leveraged by many other computerized spinal analysis tasks, such as vertebral body/intervertebrae disc segmentation [2], [3], 3D spine reconstruction [4], [5], spinal image registration [6], [7], and so on. On the other hand, it may be a crucial component of many computer-aided diagnosis and intervention systems for spinal health [8]- [13].…”

Section: Introductionmentioning

confidence: 99%

Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information

Liao¹,

Mesfin²,

Luo³

2018

IEEE Trans. Med. Imaging

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Automatic vertebrae identification and localization from arbitrary computed tomography (CT) images is challenging. Vertebrae usually share similar morphological appearance. Because of pathology and the arbitrary field-of-view of CT scans, one can hardly rely on the existence of some anchor vertebrae or parametric methods to model the appearance and shape. To solve the problem, we argue that: 1) one should make use of the short-range contextual information, such as the presence of some nearby organs (if any), to roughly estimate the target vertebrae; and 2) due to the unique anatomic structure of the spine column, vertebrae have fixed sequential order, which provides the important long-range contextual information to further calibrate the results. We propose a robust and efficient vertebrae identification and localization system that can inherently learn to incorporate both the short- and long-range contextual information in a supervised manner. To this end, we develop a multi-task 3-D fully convolutional neural network to effectively extract the short-range contextual information around the target vertebrae. For the long-range contextual information, we propose a multi-task bidirectional recurrent neural network to encode the spatial and contextual information among the vertebrae of the visible spine column. We demonstrate the effectiveness of the proposed approach on a challenging data set, and the experimental results show that our approach outperforms the state-of-the-art methods by a significant margin.

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Section: Introductionmentioning

confidence: 99%

Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information

Liao¹,

Mesfin²,

Luo³

2018

IEEE Trans. Med. Imaging

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“…It is also time-consuming and expensive for performing MRI examinations. Moreover, although some automatic and semi-automatic methods have been advanced to segment vertebrae and spinal cord from MRI images [17], there is little report on scoliosis measurements using these methods [18].…”

Section: Asterisk Indicates Corresponding Authormentioning

confidence: 99%

Automatic Measurement of Spine Curvature on 3-D Ultrasound Volume Projection Image With Phase Features

Zhou

Jiang

Lai

et al. 2017

IEEE Trans. Med. Imaging

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This paper presents an automated measurement of spine curvature by using prior knowledge on vertebral anatomical structures in ultrasound volume projection imaging (VPI). This method can be used in scoliosis assessment with free-hand 3-D ultrasound imaging. It is based on the extraction of bony features from VPI images using a newly proposed two-fold thresholding strategy, with information of the symmetric and asymmetric measures obtained from phase congruency. The spinous column profile is detected from the segmented bony regions, and it is further used to extract a curve representing spine profile. The spine curvature is then automatically calculated according to the inflection points along the curve. The algorithm was evaluated on volunteers with the different severity of scoliosis. The results obtained using the newly developed method had a good linear correlation with those by the manual method (r ≥ 0.90, p <; 0.001) and X-ray Cobb's method (r = 0.83, p <; 0.001). The bigger variations observed in the manual measurement also implied that the automatic method is more reliable. The proposed method can be a promising approach for facilitating the applications of 3-D ultrasound imaging in the diagnosis, treatment, and screening of scoliosis.

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“…An example of a T1-weighted and a T2-weighted traverse MRI images of the L3/L4 Intervertebral Disc (IVD) of the same patient are shown in Fig 1 . The algorithms may also require images with specific properties and criteria as inputs. Some algorithms require mid-sagittal MRI images as inputs [4][5][6] whereas some others require traverse images taken at certain locations as inputs [7][8][9][10]. When these algorithms were proposed in the literature, it is often assumed that a selection process has been carried out beforehand that identifies appropriate and suitable images as their input.…”

Section: Introductionmentioning

confidence: 99%

Automated selection of mid-height intervertebral disc slice in traverse lumbar spine MRI using a combination of deep learning feature and machine learning classifier

et al. 2022

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Abnormalities and defects that can cause lumbar spinal stenosis often occur in the Intervertebral Disc (IVD) of the patient’s lumbar spine. Their automatic detection and classification require an application of an image analysis algorithm on suitable images, such as mid-sagittal images or traverse mid-height intervertebral disc slices, as inputs. Hence the process of selecting and separating these images from other medical images in the patient’s set of scans is necessary. However, the technological progress in making this process automated is still lagging behind other areas in medical image classification research. In this paper, we report the result of our investigation on the suitability and performance of different approaches of machine learning to automatically select the best traverse plane that cuts closest to the half-height of an IVD from a database of lumbar spine MRI images. This study considers images features extracted using eleven different pre-trained Deep Convolution Neural Network (DCNN) models. We investigate the effectiveness of three dimensionality-reduction techniques and three feature-selection techniques on the classification performance. We also investigate the performance of five different Machine Learning (ML) algorithms and three Fully Connected (FC) neural network learning optimizers which are used to train an image classifier with hyperparameter optimization using a wide range of hyperparameter options and values. The different combinations of methods are tested on a publicly available lumbar spine MRI dataset consisting of MRI studies of 515 patients with symptomatic back pain. Our experiment shows that applying the Support Vector Machine algorithm with a short Gaussian kernel on full-length image features extracted using a pre-trained DenseNet201 model is the best approach to use. This approach gives the minimum per-class classification performance of around 0.88 when measured using the precision and recall metrics. The median performance measured using the precision metric ranges from 0.95 to 0.99 whereas that using the recall metric ranges from 0.93 to 1.0. When only considering the L3/L4, L4/L5, and L5/S1 classes, the minimum F1-Scores range between 0.93 to 0.95, whereas the median F1-Scores range between 0.97 to 0.99.

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Vertebral Column Localization, Labeling, and Segmentation

Cited by 10 publications

References 88 publications

Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information

Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information

Automatic Measurement of Spine Curvature on 3-D Ultrasound Volume Projection Image With Phase Features

Automated selection of mid-height intervertebral disc slice in traverse lumbar spine MRI using a combination of deep learning feature and machine learning classifier

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