Automatic spine segmentation from CT images using Convolutional Neural Network via redundant generation of class labels

Vania, Malinda; Mureja, Dawit; Lee, Deukhee

doi:10.1016/j.jcde.2018.05.002

Cited by 75 publications

(48 citation statements)

References 23 publications

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“…A few studies have investigated the use of CNNs for bone segmentation in CT scans. For example, Vania et al (2017) employed a CNN for the segmentation of the spine [38]. Moreover, Išgum et al (2018) proposed an iterative CNN for the segmentation of vertebrae that outperformed alternative segmentation methods [39].…”

Section: Related Workmentioning

confidence: 99%

CT image segmentation of bone for medical additive manufacturing using a convolutional neural network

Minnema

Eijnatten

Kouw

et al. 2018

Computers in Biology and Medicine

121

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Background: The most tedious and time-consuming task in medical additive manufacturing (AM) is image segmentation. The aim of the present study was to develop and train a convolutional neural network (CNN) for bone segmentation in computed tomography (CT) scans. Method: The CNN was trained with CT scans acquired using six different scanners. Standard tessellation language (STL) models of 20 patients who had previously undergone craniotomy and cranioplasty using additively manufactured skull implants served as "gold standard" models during CNN training. The CNN segmented all patient CT scans using a leave-2-out scheme. All segmented CT scans were converted into STL models and geometrically compared with the gold standard STL models. Results: The CT scans segmented using the CNN demonstrated a large overlap with the gold standard segmentation and resulted in a mean Dice similarity coefficient of 0.92 ± 0.04. The CNN-based STL models demonstrated mean surface deviations ranging between −0.19 mm ± 0.86 mm and 1.22 mm ± 1.75 mm, when compared to the gold standard STL models. No major differences were observed between the mean deviations of the CNN-based STL models acquired using six different CT scanners. Conclusions: The fully-automated CNN was able to accurately segment the skull. CNNs thus offer the opportunity of removing the current prohibitive barriers of time and effort during CT image segmentation, making patientspecific AM constructs more accesible.

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Section: Related Workmentioning

confidence: 99%

CT image segmentation of bone for medical additive manufacturing using a convolutional neural network

Minnema

Eijnatten

Kouw

et al. 2018

Computers in Biology and Medicine

121

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show abstract

“…They did this in order to minimize overfitting so that the learner could consider variabilities in vertebral width and length outside of the training dataset. Their model generated a sensitivity and specificity of 0.97 and 0.99, respectively, both of which were either better or comparable to other commonly applied methods (33). In addition to spinal segmentation, significant strides have also been made in automated detection of vertebral compression and posterior element fractures, as well FIGURE 9 | Visual representation of oversampling low incidence complications via adaptive synthetic sampling approach to imbalanced learning or ADASYN.…”

Section: Artificial Neural Networkmentioning

confidence: 91%

“…In spine surgery, computer vision technology has risen in parallel with the use of computer assisted navigation, robotic surgery, and augmented reality in the operating room, all of which require high fidelity 3D reconstructions of the spinal column from computed tomography or magnetic resonance imaging scans (33,(63)(64)(65)(66)(67). This is achieved through automated segmentation and detection of vertebrae via ANNs.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

The Role of Machine Learning in Spine Surgery: The Future Is Now

et al. 2020

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The recent influx of machine learning centered investigations in the spine surgery literature has led to increased enthusiasm as to the prospect of using artificial intelligence to create clinical decision support tools, optimize postoperative outcomes, and improve technologies used in the operating room. However, the methodology underlying machine learning in spine research is often overlooked as the subject matter is quite novel and may be foreign to practicing spine surgeons. Improper application of machine learning is a significant bioethics challenge, given the potential consequences of over-or underestimating the results of such studies for clinical decision-making processes. Proper peer review of these publications requires a baseline familiarity of the language associated with machine learning, and how it differs from classical statistical analyses. This narrative review first introduces the overall field of machine learning and its role in artificial intelligence, and defines basic terminology. In addition, common modalities for applying machine learning, including classification and regression decision trees, support vector machines, and artificial neural networks are examined in the context of examples gathered from the spine literature. Lastly, the ethical challenges associated with adapting machine learning for research related to patient care, as well as future perspectives on the potential use of machine learning in spine surgery, are discussed specifically.

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“…The disadvantage of manual segmentation is that, it is time consuming and the results are not really reproducible because the image interpretations by humans may vary significantly across interpreters [2]. Automated image segmentation could increase precision by eliminating the subjectivity of the clinician [3]. This drives the development of more efficient and robust problem-tailored image analysis methods for medical imaging.…”

Section: Introductionmentioning

confidence: 99%

Automatic Vertebral Body Segmentation using Semantic Segmentation

Arpitha*,

Rangarajan

2019

IJRTE

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Segmentation of vertebral bodies (VB) is a preliminary and useful step for the diagnosis of spine pathologies, deformations and fractures caused due to various reasons. We present a method to address this challenging problem of VB segmentation using a trending method – Semantic Segmentation (SS). The objective of semantic segmentation of images usually consisting of three main components - convolutions, downsampling, and upsampling layers is to mark every pixel of an image with a correlating class of what is being described. In this study, we developed a unique automatic semantic segmentation architecture to segment the VB from Computed Tomography (CT) images, and we compared our segmentation results with reference segmentations obtained by the experts. We evaluated the proposed method on a publicly available dataset and achieved an average accuracy of 94.16% and an average Dice Similarity Coefficient (DSC) of 93.51% for VB segmentation.

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Automatic spine segmentation from CT images using Convolutional Neural Network via redundant generation of class labels

Cited by 75 publications

References 23 publications

CT image segmentation of bone for medical additive manufacturing using a convolutional neural network

CT image segmentation of bone for medical additive manufacturing using a convolutional neural network

The Role of Machine Learning in Spine Surgery: The Future Is Now

Automatic Vertebral Body Segmentation using Semantic Segmentation

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