An Automated Deep Learning Approach for Spine Segmentation and Vertebrae Recognition Using Computed Tomography Images

Saeed, Muhammad Usman; Dikaios, Nikolaos; Dastgir, Aqsa; Ali, Ghulam; Hamid, Muhammad; Hajjej, Fahima

doi:10.3390/diagnostics13162658

Cited by 5 publications

(4 citation statements)

References 45 publications

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“…These results thus support the findings of [19] regarding HarDNet MSEG generalization and accuracy with sparse training data. The pipeline described here achieves comparable or superior results to those shown in [46,52,56] while requiring less data-a benefit to segmentation applications beyond the current study. The improved performance partly results from extending the original network architecture by providing a modified network that reads MR input layers and predicts several output tissues at once.…”

Section: Machine Learning For Image Processingmentioning

confidence: 58%

A novel CNN-based image segmentation pipeline for individualized feline spinal cord stimulation modeling

Fasse,

Newton,

Liang

et al. 2024

J. Neural Eng.

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Spinal cord stimulation (SCS) is a well-established treatment for the management of certain chronic pain conditions. More recently, it has also garnered attention as a means of modulating neural activity with the goal of restoring lost autonomic or sensory-motor function. Personalized modeling and treatment planning are critical aspects of safe and effective SCS [46, 60]. However, the generation of spine models at the required level of detail and accuracy requires time and labor intensive manual image segmentation by human experts. Hence, there is a need for maximally automated segmentation routines capable of producing high-quality anatomical models that can be used even in cases where available data is limited. To this end, we developed an automated image segmentation and model generation pipeline based on a novel Convolutional Neural Network (CNN) architecture trained on feline spinal cord magnetic resonance imaging (MRI) data. The pipeline includes steps for image preprocessing, data augmentation, transfer learning and cleanup. To assess the relative importance of each step in the pipeline and of our choice of CNN architecture, we systematically dropped steps or substituted architectures, quantifying the downstream effects in terms of tissue segmentation quality (Jaccard index and Hausdorff distance) and predicted nerve recruitment (estimated axonal depolarization). This leaveone-out analysis demonstrated that each pipeline step contributed a small but measurable increment to mean segmentation quality. Surprisingly, minor differences in segmentation accuracy translated to significant deviations (ranging between 4% and 13% for each pipeline step) in predicted nerve recruitment, highlighting the importance of careful workflow design. To our knowledge, this is the first analysis to also assess the downstream impact of segmentation quality differences on neurostimulation predictions. Furthermore, transfer learning techniques enhanced segmentation metric consistency and allowed generalization to a completely different spine region with minimal additional training data. This work helps pave the way towards fully automated, personalized SCS treatment planning in clinical settings.

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Section: Machine Learning For Image Processingmentioning

confidence: 58%

A novel CNN-based image segmentation pipeline for individualized feline spinal cord stimulation modeling

Fasse,

Newton,

Liang

et al. 2024

J. Neural Eng.

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show abstract

“…For this reason, in urgent or trauma cases, delaying care to use this technology is not reasonable. Accordingly, there is a significant effort to develop artificial intelligence-based automatic segmenting protocols, though they are largely targeting vertebral segmentation [26]. Once validated, this technology has the potential to substantially impact patient care and favorably disrupt patient selection and surgical planning for minimally invasive spine surgery patients.…”

Section: Advanced Imaging-based Surgical Planningmentioning

confidence: 99%

Pushing the Limits of Minimally Invasive Spine Surgery—From Preoperative to Intraoperative to Postoperative Management

Drossopoulos,

Sharma,

Ononogbu-Uche

et al. 2024

JCM

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The introduction of minimally invasive surgery ushered in a new era of spine surgery by minimizing the undue iatrogenic injury, recovery time, and blood loss, among other complications, of traditional open procedures. Over time, technological advancements have further refined the care of the operative minimally invasive spine patient. Moreover, pre-, and postoperative care have also undergone significant change by way of artificial intelligence risk stratification, advanced imaging for surgical planning and patient selection, postoperative recovery pathways, and digital health solutions. Despite these advancements, challenges persist necessitating ongoing research and collaboration to further optimize patient care in minimally invasive spine surgery.

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“…However, despite the relative ease of visual observation of bones in CT images, challenges such as low signal-to-noise ratio, insufficient spatial resolution, and indistinct image intensity between spongy bones and soft tissues make the precise segmentation of individual bones a complex task [40]. Accurately segmenting the spine into individual vertebrae is crucial for diagnosing spine-related illnesses, especially for detecting and classifying bone damage, fractures, lesions, and tumors [41][42][43].…”

Section: Semantic Segmentation With Aimentioning

confidence: 99%

Automatization of CT Annotation: Combining AI Efficiency with Expert Precision

Edelmers,

Kazoka,

Bolocko

et al. 2024

Diagnostics

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The integration of artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) algorithms, marks a transformative progression in medical imaging diagnostics. This technical note elucidates a novel methodology for semantic segmentation of the vertebral column in CT scans, exemplified by a dataset of 250 patients from Riga East Clinical University Hospital. Our approach centers on the accurate identification and labeling of individual vertebrae, ranging from C1 to the sacrum–coccyx complex. Patient selection was meticulously conducted, ensuring demographic balance in age and sex, and excluding scans with significant vertebral abnormalities to reduce confounding variables. This strategic selection bolstered the representativeness of our sample, thereby enhancing the external validity of our findings. Our workflow streamlined the segmentation process by eliminating the need for volume stitching, aligning seamlessly with the methodology we present. By leveraging AI, we have introduced a semi-automated annotation system that enables initial data labeling even by individuals without medical expertise. This phase is complemented by thorough manual validation against established anatomical standards, significantly reducing the time traditionally required for segmentation. This dual approach not only conserves resources but also expedites project timelines. While this method significantly advances radiological data annotation, it is not devoid of challenges, such as the necessity for manual validation by anatomically skilled personnel and reliance on specialized GPU hardware. Nonetheless, our methodology represents a substantial leap forward in medical data semantic segmentation, highlighting the potential of AI-driven approaches to revolutionize clinical and research practices in radiology.

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An Automated Deep Learning Approach for Spine Segmentation and Vertebrae Recognition Using Computed Tomography Images

Cited by 5 publications

References 45 publications

A novel CNN-based image segmentation pipeline for individualized feline spinal cord stimulation modeling

A novel CNN-based image segmentation pipeline for individualized feline spinal cord stimulation modeling

Pushing the Limits of Minimally Invasive Spine Surgery—From Preoperative to Intraoperative to Postoperative Management

Automatization of CT Annotation: Combining AI Efficiency with Expert Precision

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