Localization Of Intervertebral Discs Research Articles

Segmentation and classification of intervertebral disc using capsule stacked autoencoder

Accurate location and segmentation of IVD (intervertebral disc) are important for detecting spinal cord diseases. Despite tremendous advances in medical imaging, IVD localization and segmentation remains a hot research topic. The spine is exposed to various kinds of compression forces, which seriously causes the function of the disc and causes disorders like disc bulging and herniation. This work uses CAD (computer aided design) for IVD localization and segmentation using axial MRI images of the spine. DL (deep learning) models ensure speed and accuracy in diagnosing the process and classifying the spine problem. Hence, this work employs a DL based IVD localization and segmentation. Initially, the image is resized and enhanced by AHE (Adaptive Histogram equalization). Then, the Mask-Region-based Convolutional Neural Network (Mask R-CNN) is used to localize IVD. After localizing, the SegNet (semantic segmentation network) model efficiently carries out disc segmentation. Then the weights of SegNet are optimally fine-tuned by the adaptive rain optimization algorithm (AROA). Finally, the DL model capsule stacked autoencoder (CSAE) is used for classifying the types of diseases in IVD. The overall implementation in the Python platform and analysis in the benchmark dataset Lumbar Spine MRI achieved better accuracy and precision of 98.5% and 97.8%, respectively.

Vascular anatomy-based localization of intervertebral discs assisting needle puncture for constructing a mouse model of mechanical injury-induced lumbar intervertebral disc degeneration

Intervertebral disc degeneration (IDD) may be the primary cause of low back pain. Potential therapeutics for IDD must be validated in animal models, and their effectiveness quantified using functional metrics. Needle puncture of intervertebral discs (IVDs) has been used to induce IDD in mice and rats. Due to operational challenges, most animal IDD models are constructed using needle puncture of the caudal IVDs in mice, or by using larger animals, such as rats and rabbits. However, mouse IDD models involving lumbar IVD puncture are preferable because mice are genetically similar to humans and are the most commonly used transgenic animals, and because human IDD commonly affects the lumbar spine. We constructed a needle puncture-based mouse IDD model that relies on vascular anatomy to pinpoint lumbar IVDs. We evaluated the morphological and molecular changes in this model by using radiological, pathological, and immunostaining examinations. In our mechanical injury-induced IDD model, lumbar IVDs were accurately localized by injecting colored perfusates into the common iliac artery and vein, and right iliolumbar vein, which helped to visualize puncture positions, avoid neuromuscular injury, shorten the operation time, and decrease bleeding. Nucleus pulposus cells, defined by Krt19, and the disc height index gradually decreased after the surgery, and the degenerative effects peaked at 1 week. In conclusion, we established a mouse IDD model by performing precise puncture of lumbar IVDs via the ventral anterior approach assisted by vessel position. Our model effectively simulated the effects of IDD, and may serve as an efficient research tool.

Bimodal CT/MRI-Based Segmentation Method for Intervertebral Disc Boundary Extraction

Intervertebral disc (IVD) localization and segmentation have triggered intensive research efforts in the medical image analysis community, since IVD abnormalities are strong indicators of various spinal cord-related pathologies. Despite the intensive research efforts to address IVD boundary extraction based on MR images, the potential of bimodal approaches, which benefit from complementary information derived from both magnetic resonance imaging (MRI) and computed tomography (CT), has not yet been fully realized. Furthermore, most existing approaches rely on manual intervention or on learning, although sufficiently large and labelled 3D datasets are not always available. In this light, this work introduces a bimodal segmentation method for vertebrae and IVD boundary extraction, which requires a limited amount of intervention and is not based on learning. The proposed method comprises various image processing and analysis stages, including CT/MRI registration, Otsu-based thresholding and Chan–Vese-based segmentation. The method was applied on 98 expert-annotated pairs of CT and MR spinal cord images with varying slice thicknesses and pixel sizes, which were obtained from 7 patients using different scanners. The experimental results had a Dice similarity coefficient equal to 94.77(%) for CT and 86.26(%) for MRI and a Hausdorff distance equal to 4.4 pixels for CT and 4.5 pixels for MRI. Experimental comparisons with state-of-the-art CT and MRI segmentation methods lead to the conclusion that the proposed method provides a reliable alternative for vertebrae and IVD boundary extraction. Moreover, the segmentation results are utilized to perform a bimodal visualization of the spine, which could potentially aid differential diagnosis with respect to several spine-related pathologies.

Learning–based vertebra localization and labeling in 3D CT data of possibly incomplete and pathological spines

Background and objectiveWe present a fully automatic system based on learning approaches, which aims to localization and identification (labeling) of vertebrae in 3D computed tomography (CT) scans of possibly incomplete spines in patients with bone metastases and vertebral compressions. MethodsThe framework combines a set of 3D algorithms for i) spine detection using a convolution neural network (CNN) ii) spinal cord tracking based on combination of a CNN and a novel growing sphere method with a population optimization, iii) intervertebral discs localization using a novel approach of spatially variant filtering of intensity profiles and iv) vertebra labeling using a CNN-based classification combined with global dynamic optimization. ResultsThe proposed algorithm has been validated in testing databases, including also a publicly available dataset. The mean error of intervertebral discs localization is 4.4 mm, and for vertebra labeling, the average rate of correctly identified vertebrae is 87.1%, which can be considered a good result with respect to the large share of highly distorted spines and incomplete spine scans. ConclusionsThe proposed framework, which combines several advanced methods including also three CNNs, works fully automatically even with incomplete spine scans and with distorted pathological cases. The achieved results allow including the presented algorithms as the first phase to the fully automated computer-aided diagnosis (CAD) system for automatic spine-bone lesion analysis in oncological patients.

Fully automatic cross-modality localization and labeling of vertebral bodies and intervertebral discs in 3D spinal images.

We present a cross-modality and fully automatic pipeline for labeling of intervertebral discs and vertebrae in volumetric data of the lumbar and thoracolumbar spine. The main goal is to provide an algorithm that is applicable to a wide range of different sequences and acquisition protocols, like T1- and T2- weighted MR scans, MR Dixon data, and CT scans. This requires that the learned models generalize without retraining to modalities and scans with unseen image contrasts. We address this challenge by automatically localizing the sacral region combining local entropy-optimized texture models with convolutional neural networks. For subsequent labeling, local three-disc entropy models are matched iteratively to the spinal column. Every model-matched position is further refined by an intensity-based template-matching approach, based solely on the reduced intensity scale provided by the entropy models. We evaluated our method on 161 publicly available scans, acquired on various scanners. We showed that our method can deal with a wide range of different MR protocols as well as with CT data. We achieved a sacrum detection rate of 93.6%. Mean center accuracies ranged from 2.5±1.5 to 5.7±3.8mm for the different sets of scans. We present a novel spine labeling framework that is applicable to a highly heterogeneous set of scans without retraining of the method. Our approach achieves high sacrum localization accuracy and shows promising labeling results. To the best of our knowledge, an algorithm able to deal with such a diverse set of MR and CT scans has not yet been presented in the literature.

3D multi-scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images.

Intervertebral discs (IVDs) are small joints that lie between adjacent vertebrae. The localization and segmentation of IVDs are important for spine disease diagnosis and measurement quantification. However, manual annotation is time-consuming and error-prone with limited reproducibility, particularly for volumetric data. In this work, our goal is to develop an automatic and accurate method based on fully convolutional networks (FCN) for the localization and segmentation of IVDs from multi-modality 3D MR data. Compared with single modality data, multi-modality MR images provide complementary contextual information, which contributes to better recognition performance. However, how to effectively integrate such multi-modality information to generate accurate segmentation results remains to be further explored. In this paper, we present a novel multi-scale and modality dropout learning framework to locate and segment IVDs from four-modality MR images. First, we design a 3D multi-scale context fully convolutional network, which processes the input data in multiple scales of context and then merges the high-level features to enhance the representation capability of the network for handling the scale variation of anatomical structures. Second, to harness the complementary information from different modalities, we present a random modality voxel dropout strategy which alleviates the co-adaption issue and increases the discriminative capability of the network. Our method achieved the 1st place in the MICCAI challenge on automatic localization and segmentation of IVDs from multi-modality MR images, with a mean segmentation Dice coefficient of 91.2% and a mean localization error of 0.62 mm. We further conduct extensive experiments on the extended dataset to validate our method. We demonstrate that the proposed modality dropout strategy with multi-modality images as contextual information improved the segmentation accuracy significantly. Furthermore, experiments conducted on extended data collected from two different time points demonstrate the efficacy of our method on tracking the morphological changes in a longitudinal study.

MRI Evaluation of Lumbar Disc Degenerative Disease

Aim of the work: lumbar disc degenerative disease is most leading cause of back pain, it is the most common cause of disability at age above 45 years and 2nd most common reason for primary care physician visit. Magnetic Resonance Imaging (MRI) is the standard imaging modality for detecting disc pathology due to its advantage of lack of radiation, multi planar imaging capability, excellent spinal soft-tissue contrast and precise localization of intervertebral discs’ changes. this study aimed to evaluate the characterization, extent and changes associated with the degenerative lumbar disc disease by Magnetic Resonance Imaging. Study Design: cross-sectional and observational study. Study Sample: forty consecutive and clinically examined patients with lower back pain suggestive of lumbar disc disease. Material and Methods: 40 patients with age group between 17 and 80 years with low back pain were diagnosed on 1.5 Tesla MRI machine. The following MRI findings were noted: lumbar lordosis preserved or not, ligamentum flavum hypertrophy, facet joint arthrosis or osteophytes formation and Endplate (modic) changes, decreased disc height as compared to the upper and lower vertebral levels and loss of signals, disc bulge at different levels, Disc protrusion and disc extrusion was observed. Result: most of patient showed lossof lumbar lordosis, facet joint arthrosis, ligamentum flavum hypertrophy, Endplate (modic) changes, and osteophytes formation. Disc dehydration which is one of the earliest features of aging and disc degeneration was present in (42%) patients with the most dehydrated disc seen at L4/L5 and L5/S1. Patients with degenerative disc disease had the following disc contour abnormalities. Disc bulges (26.5%) and herniations (10.5%). Herniations were further reported as broad based herniations (9.5%), extrusions (19.0%) and the protrusions (71.4%). The most common site for bulges and herniations was L4/L5 (33.6%) and (57.1%) respectively. The most common complication of bulges and herniations was impingement of exiting nerve roots (40%) and compression of exiting nerve roots and cauda equina (47.6%) respectively. Conclusion: lumbar disc degeneration is the most common cause of low back pain. It is the most common cause of disability at age above 45 years. Plain radiograph can be helpful, but MRI is the standard imaging modality for detecting disc pathology due to its advantage of lack of radiation, multi planar imaging capability, excellent spinal soft-tissue contrast and precise localization of intervertebral discs changes.

A method of localization and segmentation of intervertebral discs in spine MRI based on Gabor filter bank.

BackgroundSpine magnetic resonance image (MRI) plays a very important role in the diagnosis of various spinal diseases, such as disc degeneration, scoliosis, and osteoporosis. Accurate localization and segmentation of the intervertebral disc (IVD) in spine MRI can help accelerate the diagnosis time and assist in the treatment by providing quantitative parameters. In this paper, a method based on Gabor filter bank is proposed for IVD localization and segmentation.MethodsFirst, the structural features of IVDs are extracted using a Gabor filter bank. Second, the Gabor features of spine are calculated and spinal curves are detected. Third, the Gabor feature images (GFI) of IVDs are calculated and adjusted according to the spinal curves. Fourth, the IVDs are localized by clustering analysis with GFI. Finally, an optimum grayscale-based algorithm with self-adaptive threshold, combined with the localization results and Gabor features of the spine, is performed for IVDs segmentation.ResultsThe proposed method is verified by an MRI dataset consisting of 278 IVDs from 37 patients. The accuracy of localization is 98.23 % and the dice similarity index for segmentation evaluation is 0.9237.ConclusionsThe proposed Gabor filter based method is effective for IVD localization and segmentation. It would be useful in computer-aided diagnosis of IVD diseases and computer-assisted spine surgery.

On computerized methods for spine analysis in MRI: a systematic review.

In the last decades, the increasing medical interest in magnetic resonance imaging (MRI) of the spine gave rise to a growing number of publications on computerized methods for spine analysis, covering goals such as localization and segmentation of vertebrae and intervertebral discs as well as the extraction and segmentation of the spinal canal and cord. We provide a critical systematic review to work in the field, putting focus on approaches that can be applied to different imaging sequences and settings. Work is analysed on two levels. First, methods are reviewed in detail so that the reader understands justifications and constraints of particular work. Second, work is classified according to relevant attributes and tabulated to give an impression on recent trends. We discuss the general methodical and evaluational aspects of the work as well as challenges specific to MRI such as the lack of intensity standardization and partial volume effects. Methods can be condensed to a small number of optimization frameworks, e.g.,graphical models, cost-minimal paths and deformable models. Works sharing the same framework mainly differentiate by the types of information, i.e.,pose, geometry and appearance, that are used and by the implementation thereof. MRI-specific challenges are rarely addressed explicitly, calling into question the applicability of most methods to changing imaging sequences or settings. Most often, little attention is paid to evaluation, meaning that results lack comparability and reproducibility although publicly available data sets exist. The diversity of MRI sequences and settings still poses challenges to computerized spine analysis. Further research is necessary to implement methods that are actually applicable in practice, e.g.,in clinical routine or for study purposes. Certainly, manual guidance will be necessary at some point, for instance to deal with changing subject positions. Therefore, future work should put attention to the appropriate integration of manual interaction.

Localization and Segmentation of 3D Intervertebral Discs in MR Images by Data Driven Estimation.

This paper addresses the problem of fully-automatic localization and segmentation of 3D intervertebral discs (IVDs) from MR images. Our method contains two steps, where we first localize the center of each IVD, and then segment IVDs by classifying image pixels around each disc center as foreground (disc) or background. The disc localization is done by estimating the image displacements from a set of randomly sampled 3D image patches to the disc center. The image displacements are estimated by jointly optimizing the training and test displacement values in a data-driven way, where we take into consideration both the training data and the geometric constraint on the test image. After the disc centers are localized, we segment the discs by classifying image pixels around disc centers as background or foreground. The classification is done in a similar data-driven approach as we used for localization, but in this segmentation case we are aiming to estimate the foreground/background probability of each pixel instead of the image displacements. In addition, an extra neighborhood smooth constraint is introduced to enforce the local smoothness of the label field. Our method is validated on 3D T2-weighted turbo spin echo MR images of 35 patients from two different studies. Experiments show that compared to state of the art, our method achieves better or comparable results. Specifically, we achieve for localization a mean error of 1.6-2.0 mm, and for segmentation a mean Dice metric of 85%-88% and a mean surface distance of 1.3-1.4 mm.

MRI evaluation of lumbar disc degenerative disease.

Lower back pain secondary to degenerative disc disease is a condition that affects young to middle-aged persons with peak incidence at approximately 40 y. MRI is the standard imaging modality for detecting disc pathology due to its advantage of lack of radiation, multiplanar imaging capability, excellent spinal soft-tissue contrast and precise localization of intervertebral discs changes. To evaluate the characterization, extent, and changes associated with the degenerative lumbar disc disease by Magnetic Resonance Imaging. Cross-sectional and observational study. A total 109 patients of the lumbar disc degeneration with age group between 17 to 80 y were diagnosed & studied on 1.5 Tesla Magnetic Resonance Imaging machine. MRI findings like lumbar lordosis, Schmorl's nodes, decreased disc height, disc annular tear, disc herniation, disc bulge, disc protrusion and disc extrusion were observed. Narrowing of the spinal canal, lateral recess and neural foramen with compression of nerve roots observed. Ligamentum flavum thickening and facetal arthropathy was observed. Males were more commonly affected in Degenerative Spinal Disease & most of the patients show loss of lumbar lordosis. Decreased disc height was common at L5-S1 level. More than one disc involvement was seen per person. L4 - L5 disc was the most commonly involved. Annular disc tear, disc herniation, disc extrusion, narrowing of spinal canal, narrowing of lateral recess, compression of neural foramen, ligamentum flavum thickening and facetal arthropathy was common at the L4 -L5 disc level. Disc buldge was common at L3 - L4 & L4 - L5 disc level. Posterior osteophytes are common at L3 - L4 & L5 -S1 disc level. L1- L2 disc involvement and spondylolisthesis are less common. Lumbar disc degeneration is the most common cause of low back pain. Plain radiograph can be helpful in visualizing gross anatomic changes in the intervertebral disc. But, MRI is the standard imaging modality for detecting disc pathology due to its advantage of lack of radiation, multiplanar imaging capability, excellent spinal soft-tissue contrast and precise localization of intervertebral discs changes.

Supervised methods for detection and segmentation of tissues in clinical lumbar MRI

Lower back pain (LBP) is widely prevalent all over the world and more than 80% of the people suffer from LBP at some point of their lives. Moreover, a shortage of radiologists is the most pressing cause for the need of CAD (computer-aided diagnosis) systems. Automatic localization and labeling of intervertebral discs from lumbar MRI is the first step towards computer-aided diagnosis of lower back ailments. Subsequently, for diagnosis and characterization (quantification and localization) of abnormalities like disc herniation and stenosis, a completely automatic segmentation of intervertebral discs and the dural sac is extremely important. Contribution of this paper towards clinical CAD systems is two-fold. First, we propose a method to automatically detect all visible intervertebral discs in clinical sagittal MRI using heuristics and machine learning techniques. We provide a novel end-to-end framework that outputs a tight bounding box for each disc, instead of simply marking the centroid of discs, as has been the trend in the recent past. Second, we propose a method to simultaneously segment all the tissues (vertebrae, intervertebral disc, dural sac and background) in a lumbar sagittal MRI, using an auto-context approach instead of any explicit shape features or models. Past work tackles the lumbar segmentation problem on a tissue/organ basis, and which tend to perform poorly in clinical scans due to high variability in appearance. We, on the other hand, train a series of robust classifiers (random forests) using image features and sparsely sampled context features, which implicitly represent the shape and configuration of the image. Both these methods have been tested on a huge clinical dataset comprising of 212 cases and show very promising results for both disc detection (98% disc localization accuracy and 2.08mm mean deviation) and sagittal MRI segmentation (dice similarity indices of 0.87 and 0.84 for the dural sac and the inter-vertebral disc, respectively).

Simultaneous Localization of Lumbar Vertebrae and Intervertebral Discs With SVM-Based MRF

This paper presents a method for localizing and labeling the lumbar vertebrae and intervertebral discs in mid-sagittal MR image slices. The approach is based on a Markov-chain-like graphical model of the ordered discs and vertebrae in the lumbar spine. The graphical model is formulated by combining local image features and semiglobal geometrical information. The local image features are extracted from the image by employing pyramidal histogram of oriented gradients (PHOG) and a novel descriptor that we call image projection descriptor (IPD). These features are trained with support vector machines (SVM) and each pixel in the target image is locally assigned a score. These local scores are combined with the semiglobal geometrical information like the distance ratio and angle between the neighboring structures under the Markov random field (MRF) framework. An exact localization of discs and vertebrae is inferred from the MRF by finding a maximum a posteriori solution efficiently using dynamic programming. As a result of the novel features introduced, our system can scale-invariantly localize discs and vertebra at the same time even in the existence of missing structures. The proposed system is tested and validated on a clinical lumbar spine MR image dataset containing 80 subjects of which 64 have disc- and vertebra-related diseases and abnormalities. The experiments show that our system is successful even in abnormal cases and our results are comparable to the state of the art.