Combining convolutional neural networks and star convex cuts for fast whole spine vertebra segmentation in MRI

Rak, Marko; Steffen, Johannes; Meyer, Anneke; Hansen, Christian; Tönnies, Klaus D.

doi:10.1016/j.cmpb.2019.05.003

Cited by 33 publications

(19 citation statements)

References 22 publications

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“…Furthermore, the mean Dice coefficient was 0.912±0.062 between U-Net ROIs and the manual ROIs in the validation cohort. The capability of automatic segmentation of vertebrae in fat fraction map in our work was similar with previous studies that used T1-weighted or T2-weighted images for spine segmentation (28,29).…”

Section: Discussionsupporting

confidence: 84%

Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence

Zhao¹,

Zhao²,

Chen³

et al. 2022

Quant Imaging Med Surg

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Background: Although lumbar bone marrow fat fraction (BMFF) has been demonstrated to be predictive of osteoporosis, its utility is limited by the requirement of manual segmentation. Additionally, quantitative features beyond simple BMFF average remain to be explored. In this study, we developed a fully automated radiomic pipeline using deep learning-based segmentation to detect osteoporosis and abnormal bone density (ABD) using a <20 s modified Dixon (mDixon) sequence.Methods: In total, 222 subjects underwent quantitative computed tomography (QCT) and lower back magnetic resonance imaging (MRI). Bone mineral density (BMD) were extracted from L1-L3 using QCT as the reference standard; 206 subjects (48.8±14.9 years old, 140 females) were included in the final analysis, and were divided temporally into the training/validation set (142/64 subjects). A deep-learning network was developed to perform automated segmentation. Radiomic models were built using the same training set to predict ABD and osteoporosis using the mDixon maps. The performance was evaluated using the temporal validation set comprised of 64 subjects, along with the automated segmentation. Additional 25 subjects (56.1±8.8 years, 14 females) from another site and a different scanner vendor was included as independent validation to evaluate the performance of the pipeline. Results:The automated segmentation achieved an outstanding mean dice coefficient of 0.912±0.062 compared to manual in the temporal validation. Task-based evaluation was performed in the temporal validation set, for predicting ABD and osteoporosis, the area under the curve, sensitivity, specificity,

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Section: Discussionsupporting

confidence: 84%

Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence

Zhao¹,

Zhao²,

Chen³

et al. 2022

Quant Imaging Med Surg

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“…The mean DSC obtained for AIS and healthy volunteers' vertebral bodies (about 93-94% DSC) is comparable to the current state-of-the-art [27] and significantly higher relative to the previous study focusing on AIS patients [25], which reported DSC values between 80 and 90%. A performance similar to this paper was also reported by Korez et al [28] and by Lu et al [26].…”

Section: E Qualitative Analysis After Post-processingsupporting

confidence: 77%

“…Several deep-learning-based approaches were reported in the literature for the vertebral anatomy segmentation from CT, including classification-based methods [19] and 2D/3D CNNs, such as state-of-the-art algorithms (UNet [20]) [21]- [23] or their variants [24]. 2D UNets [25], [26] and hybrid approaches combining CNN with model-based segmentation or star convex graph cuts [27], [28] were detailed for MRI-based vertebral body automatic segmentation and achieved the highest performance for this task. The approach developed by Rak.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automatic Segmentation for Reconstructing Vertebral Anatomy of Healthy Adolescents and Patients With Adolescent Idiopathic Scoliosis (AIS) Using MRI Data

et al. 2021

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MRI is a non-ionising imaging modality that could be used as an alternative to Xray-based imaging methods to accurately assess the 3D morphology of the vertebral anatomy of scoliosis patients. However, a major caveat in utilising MRI is the significant amount of time required to manually segment the anatomy of interest. To overcome this limitation, we implemented a fully automatic method for the 3D segmentation of thoracic vertebrae, including vertebral body and posterior elements, of healthy adolescents and patients with Adolescent Idiopathic Scoliosis (AIS) using MRI data. 62 MRI scans were obtained from 3 healthy volunteers and 25 patients with AIS. A state-of-the-art deep-learning network for segmentation was trained using image patches of the apical vertebra (T7, T8, T9 or T10) extracted from 20 AIS patient MRIs. Ad-hoc data augmentation was adopted to represent the unlabeled vertebral levels in the dataset (T5-T6, T11-T12). The vertebral levels T5-T12 were then segmented for the remaining MRI datasets by feeding to the network the MRI patches generated by translating a window of fixed size and stride onto the MRI volume. The mean dice score coefficient for the AIS patient vertebral levels T5-T12 was of 87%±4.3%, which was comparable to the performance achieved by two experts. On average, 93% and 97% of the MRI segmented slices were considered clinically acceptable morphological reconstructions of AIS and healthy volunteer vertebrae, respectively. The proposed method can be considered as the first step towards more routine MRIbased imaging of AIS osseous deformities, reducing the cumulative exposure of young patients to ionising radiation.

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“…There are a wide variety of medical imaging modalities and magnetic resonance imaging (MRI) is majorly applied for clinical diagnosis and prog - nosis. Recently, some studies have demonstrated suc - cessful application of artificial intelligence algorithms for spine medical image segmentation [ 17 , 19 , 20 , 22 , 24 , 27 , 3 3 , 35 , 38 , 47 , 49 ], computer-aided spine diagnosis [ 84 - 87 ], and disease detection and classification [ 10 , 45 ]. In other words, spinal images could be analyzed, processed, and categorized by using neural network.…”

Section: Discussionmentioning

confidence: 99%

A Review on the Use of Artificial Intelligence in Spinal Diseases

et al. 2020

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Artificial neural networks (ANNs) have been used in a wide variety of real-world applications and it emerges as a promising field across various branches of medicine. This review aims to identify the role of ANNs in spinal diseases. Literature were searched from electronic databases of Scopus and Medline from 1993 to 2020 with English publications reported on the application of ANNs in spinal diseases. The search strategy was set as the combinations of the following keywords: “artificial neural networks,” “spine,” “back pain,” “prognosis,” “grading,” “classification,” “prediction,” “segmentation,” “biomechanics,” “deep learning,” and “imaging.” The main findings of the included studies were summarized, with an emphasis on the recent advances in spinal diseases and its application in the diagnostic and prognostic procedures. According to the search strategy, a set of 3,653 articles were retrieved from Medline and Scopus databases. After careful evaluation of the abstracts, the full texts of 89 eligible papers were further examined, of which 79 articles satisfied the inclusion criteria of this review. Our review indicates several applications of ANNs in the management of spinal diseases including (1) diagnosis and assessment of spinal disease progression in the patients with low back pain, perioperative complications, and readmission rate following spine surgery; (2) enhancement of the clinically relevant information extracted from radiographic images to predict Pfirrmann grades, Modic changes, and spinal stenosis grades on magnetic resonance images automatically; (3) prediction of outcomes in lumbar spinal stenosis, lumbar disc herniation and patient-reported outcomes in lumbar fusion surgery, and preoperative planning and intraoperative assistance; and (4) its application in the biomechanical assessment of spinal diseases. The evidence suggests that ANNs can be successfully used for optimizing the diagnosis, prognosis and outcome prediction in spinal diseases. Therefore, incorporation of ANNs into spine clinical practice may improve clinical decision making.

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Combining convolutional neural networks and star convex cuts for fast whole spine vertebra segmentation in MRI

Cited by 33 publications

References 22 publications

Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence

Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence

Deep Learning-Based Automatic Segmentation for Reconstructing Vertebral Anatomy of Healthy Adolescents and Patients With Adolescent Idiopathic Scoliosis (AIS) Using MRI Data

A Review on the Use of Artificial Intelligence in Spinal Diseases

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