Segmentation Of Intervertebral Discs Research Articles

Evaluation of the reliability of measuring lower back muscles cross-sectional area based on manual segmentation within multi-level MRI images

IntroductionManual segmentation of paraspinal muscle cross-sectional area (CSA) is widely used to assess related health disorders. This study aimed to evaluate the reliability of this segmentation process for each paraspinal region of interest across the three intervertebral levels commonly used for segmentation (L3/4, L4/5, and L5/S1). MethodsAxial-T2-weighted MRI images for 238 patients were divided among five raters (47 ± 1 cases each). To conduct the intra-rater reliability study, the CSA of each paraspinal lumber muscle (psoas major (PM), multifidus (MF), and erector spinae (ES)) and the intervertebral disc (ID) were manually segmented twice on all targeted levels before being assessed for each region per rater. The Inter-rater reliability was determined by comparing the results of different readers who segmented the same dataset. The Intraclass Correlation (ICC) and Coefficient-of-Variation percentage (CV%) were reported for each analysis. ResultsLow intra- and inter-rater variability (CV%<11) was found for each reader in each region and intervertebral level. The inter-rater reliability was excellent (ICC>0.9) for the PM, ID, and ES at L3/4. However, it was very good for MF at all levels and ES at L4/5, L5/S1 (ICC range: 0.82–0.88) affected by the fat-infiltration nature of the ES and MF muscles, their proximity to each other, and their smaller size (correlation between muscle size and ICC = 0.6, P < 0.01). The ID segmentation has the lowest CV (<3 %) and excellent ICC (>0.93). ConclusionManual paraspinal muscle segmentation using axial-T2-weighted MRI is reliable at all commonly segmented intervertebral levels. However, the reliability level can be degraded by the presence of high-fat infiltrate, unclear muscle boundaries, and muscle size. Implications for practiceFollowing consistent guidelines can help improve segmentation results. The IDs can be used as reliable internal references.

Automated Three-Dimensional Imaging and Pfirrmann Classification of Intervertebral Disc Using a Graphical Neural Network in Sagittal Magnetic Resonance Imaging of the Lumbar Spine.

This study aimed to develop a graph neural network (GNN) for automated three-dimensional (3D) magnetic resonance imaging (MRI) visualization and Pfirrmann grading of intervertebral discs (IVDs), and benchmark it against manual classifications. Lumbar IVD MRI data from 300 patients were retrospectively analyzed. Two clinicians assessed the manual segmentation and grading for inter-rater reliability using Cohen's kappa. The IVDs were then processed and classified using an automated convolutional neural network (CNN)-GNN pipeline, and their performance was evaluated using F1 scores. Manual Pfirrmann grading exhibited moderate agreement (κ = 0.455-0.565) among the clinicians, with higher exact match frequencies at lower lumbar levels. Single-grade discrepancies were prevalent except at L5/S1. Automated segmentation of IVDs using a pretrained U-Net model achieved an F1 score of 0.85, with a precision and recall of 0.83 and 0.88, respectively. Following 3D reconstruction of the automatically segmented IVD into a 3D point-cloud representation of the target intervertebral disc, the GNN model demonstrated moderate performance in Pfirrmann classification. The highest precision (0.81) and F1 score (0.71) were observed at L2/3, whereas the overall metrics indicated moderate performance (precision: 0.46, recall: 0.47, and F1 score: 0.46), with variability across spinal levels. The integration of CNN and GNN offers a new perspective for automating IVD analysis in MRI. Although the current performance highlights the need for further refinement, the moderate accuracy of the model, combined with its 3D visualization capabilities, establishes a promising foundation for more advanced grading systems.

Recognition of the lumbar spine using MRI plane-based FiLM conditioning and patient dependent batching on semantic segmentation

Abstract Lower back pain (LBP) is a common symptom that can be linked to the degeneration of intervertebral discs or injuries to vertebrae. Accurate segmentation of lumbar spinal structures is vital for image-based surgery planning but can be resource intensive. Recent studies have shown that U-Net models can be used reliably to segment vertebrae and intervertebral discs from medical imaging data. This study investigated the potential of combining multiple planar MRI images to segment these structures automatically. The study used MRI data from 83 patients with back pain of the lower spine, with ground truth segmentations performed by a spine surgery specialist using Mimics software. The segmentation models were trained using a specialized patient batching method and a FiLMed U-Net model, which applies a Feature-wise Linear Modulation layer to improve mask generation. The models were trained using a 10-fold cross validation study, and the dice loss was used as the objective function. The study found that both patient batching and FiLM layers improve results by around 5% compared to a baseline U-Net. The average IoU scores of the patient batching method were 0.896 for vertebrae and 0.876 intervertebral disc segmentation. The FiLMed U-Net model achieved average IoU scores of 0.844 and 0.814 and against a baseline of 0.837 and 0.774, respectively. A web-based tool was developed to enable medical personnel to assess segmentation quality visually. Overall, the study indicates that U-Nets show better segmentation potential when using imaging data with complete 3D information of one patient. Future studies should focus on increasing the dataset size and exploring other 3D model architectures to improve segmentation performance.

An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging

Background and objective:Accurate IVD segmentation is crucial for diagnosing and treating spinal conditions. Traditional deep learning methods depend on extensive, annotated datasets, which are hard to acquire. This research proposes an intensity-based self-supervised domain adaptation, using unlabeled multi-domain data to reduce reliance on large annotated datasets.Methods:The study introduces an innovative method using intensity-based self-supervised learning for IVD segmentation in MRI scans. This approach is particularly suited for IVD segmentations due to its ability to effectively capture the subtle intensity variations that are characteristic of spinal structures. The model, a dual-task system, simultaneously segments IVDs and predicts intensity transformations. This intensity-focused method has the advantages of being easy to train and computationally light, making it highly practical in diverse clinical settings. Trained on unlabeled data from multiple domains, the model learns domain-invariant features, adeptly handling intensity variations across different MRI devices and protocols.Results:Testing on three public datasets showed that this model outperforms baseline models trained on single-domain data. It handles domain shifts and achieves higher accuracy in IVD segmentation.Conclusions:This study demonstrates the potential of intensity-based self-supervised domain adaptation for IVD segmentation. It suggests new directions for research in enhancing generalizability across datasets with domain shifts, which can be applied to other medical imaging fields.

Development of a software system for surgical robots based on multimodal image fusion: study protocol.

Surgical robots are gaining increasing popularity because of their capability to improve the precision of pedicle screw placement. However, current surgical robots rely on unimodal computed tomography (CT) images as baseline images, limiting their visualization to vertebral bone structures and excluding soft tissue structures such as intervertebral discs and nerves. This inherent limitation significantly restricts the applicability of surgical robots. To address this issue and further enhance the safety and accuracy of robot-assisted pedicle screw placement, this study will develop a software system for surgical robots based on multimodal image fusion. Such a system can extend the application range of surgical robots, such as surgical channel establishment, nerve decompression, and other related operations. Initially, imaging data of the patients included in the study are collected. Professional workstations are employed to establish, train, validate, and optimize algorithms for vertebral bone segmentation in CT and magnetic resonance (MR) images, intervertebral disc segmentation in MR images, nerve segmentation in MR images, and registration fusion of CT and MR images. Subsequently, a spine application model containing independent modules for vertebrae, intervertebral discs, and nerves is constructed, and a software system for surgical robots based on multimodal image fusion is designed. Finally, the software system is clinically validated. We will develop a software system based on multimodal image fusion for surgical robots, which can be applied to surgical access establishment, nerve decompression, and other operations not only for robot-assisted nail placement. The development of this software system is important. First, it can improve the accuracy of pedicle screw placement, percutaneous vertebroplasty, percutaneous kyphoplasty, and other surgeries. Second, it can reduce the number of fluoroscopies, shorten the operation time, and reduce surgical complications. In addition, it would be helpful to expand the application range of surgical robots by providing key imaging data for surgical robots to realize surgical channel establishment, nerve decompression, and other operations.

Cervical Intervertebral Disc Segmentation Based on Multi-Scale Information Fusion and Its Application

The cervical intervertebral disc, a cushion-like element between the vertebrae, plays a critical role in spinal health. Investigating how to segment these discs is crucial for identifying abnormalities in cervical conditions. This paper introduces a novel approach for segmenting cervical intervertebral discs, utilizing a framework based on multi-scale information fusion. Central to this approach is the integration of multi-level features, both low and high, through an encoding–decoding process, combined with multi-scale semantic fusion, to progressively refine the extraction of segmentation characteristics. The multi-scale semantic fusion aspect of this framework is divided into two phases: one leveraging convolution for scale interaction and the other utilizing pooling. This dual-phase method markedly improves segmentation accuracy. Facing a shortage of datasets for cervical disc segmentation, we have developed a new dataset tailored for this purpose, which includes interpolation between layers to resolve disparities in pixel spacing along the longitudinal and transverse axes in CT image sequences. This dataset is good for advancing cervical disc segmentation studies. Our experimental findings demonstrate that our network model not only achieves good segmentation accuracy on human cervical intervertebral discs but is also highly effective for three-dimensional reconstruction and printing applications. The dataset will be publicly available soon.

Accurate Intervertebral Disc Segmentation Approach Based on Deep Learning.

Automatically segmenting specific tissues or structures from medical images is a straightforward task for deep learning models. However, identifying a few specific objects from a group of similar targets can be a challenging task. This study focuses on the segmentation of certain specific intervertebral discs from lateral spine images acquired from an MRI scanner. In this research, an approach is proposed that utilizes MultiResUNet models and employs saliency maps for target intervertebral disc segmentation. First, a sub-image cropping method is used to separate the target discs. This method uses MultiResUNet to predict the saliency maps of target discs and crop sub-images for easier segmentation. Then, MultiResUNet is used to segment the target discs in these sub-images. The distance maps of the segmented discs are then calculated and combined with their original image for data augmentation to predict the remaining target discs. The training set and test set use 2674 and 308 MRI images, respectively. Experimental results demonstrate that the proposed method significantly enhances segmentation accuracy to about 98%. The performance of this approach highlights its effectiveness in segmenting specific intervertebral discs from closely similar discs.

SALW-Net: a lightweight convolutional neural network based on self-adjusting loss function for spine MR image segmentation.

Segmentation of intervertebral discs and vertebrae from spine magnetic resonance (MR) images is essential to aid diagnosis algorithms for lumbar disc herniation. Convolutional neural networks (CNN) are effective methods, but often require high computational costs. Designing a lightweight CNN is more suitable for medical sites lacking high-computing power devices, yet due to the unbalanced pixel distribution in spine MR images, the segmentation is often sub-optimal. To address this issue, a lightweight spine segmentation CNN based on a self-adjusting loss function, which is named SALW-Net, is proposed in this study. For SALW-Net, the self-adjusting loss function could dynamically adjust the loss weights of the two branches according to the differences in segmentation results and labels during the training; thus, the ability for learning unbalanced pixels is enhanced. Two separate datasets are used to evaluate the proposed SALW-Net. Specifically, the proposed SALW-Net has fewer parameter numbers than U-net (only 2%) but achieves higher evaluation scores than that of U-net (the average DSC score of SALW-Net is 0.8781, and that of U-net is 0.8482). In addition, the practicality validation for SALW-Net is also proceeding, including deploying the model on a lightweight device and producing an aid diagnosis algorithm based on segmentation results. This means our SALW-Net has clinical application potential for assisted diagnosis in low computational power scenarios.

An effective U-Net and BiSeNet complementary network for spine segmentation

Spine segmentation with MRI plays an important role in the diagnosis and treatment of various spinal diseases, especially for multi-class segmentation of different vertebrae and intervertebral discs. However, due to the similarity between classes and differences within classes of spinal images, it is still a challenge to realize accurate multi-class fusion segmentation of different vertebrae and intervertebral discs. In this paper, we propose an effective U-Net and BiSeNet complementary network for spine segmentation to fully exploit their advantages in feature extraction. The network consists of a residual U-Net, a spatial feature extractor, and a feature fusion extractor. The residual U-Net selects the U-Net framework based on ResNet34, aiming at fully extracting the features of vertebrae and intervertebral discs in MRI. The spatial feature extractor takes the strip pooling (SP) blocks to replace the spatial extraction path in BiSeNet network, aiming at capturing long-range relations among the vertebrae and intervertebral discs and the interconnections of same class. The feature fusion extractor adopts the attention refinement modules (ARM) to aggregate the features from the residual U-Net and the spatial feature extractor, aiming at guiding the network to learn the distinguishing features between vertebrae and intervertebral discs by attention mechanism and making full use of the complementary features of the two paths. Experiments on MRI of 172 subjects show that our proposed network achieves impressive performance with mean Dice similarity coefficients of 82.802 % in all spinal structures. The proposed method has great value in the diagnosis and treatment of spinal diseases.

Automatic generation of subject-specific finite element models of the spine from magnetic resonance images.

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echo-gradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deep-learning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93-0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models.

MAS-Net:Multi-modal Assistant Segmentation Network For Lumbar Intervertebral Disc.

Objective.Despite advancements in medical imaging technology, the diagnosis and positioning of lumbar disc diseases still heavily rely on the expertise and experience of medical professionals. This process is often time-consuming, labor-intensive, and susceptible to subjective factors. Achieving automatic positioning and segmentation of lumbar intervertebral disc (LID) is the first and critical step in intelligent diagnosis of lumbar disc diseases. However, due to the complexity of the vertebral body and the ambiguity of the soft tissue boundaries of the LID, accurate and intelligent segmentation of LIDs remains challenging. The study aims to accurately and intelligently segment and locate LIDs by fully utilizing multi-modal lumbar magnetic resonance Images (MRIs).Approach.A novel multi-modal assistant segmentation network (MAS-Net) is proposed in this paper. The architecture consists of four key components: the multi-branch fusion encoder (MBFE), the cross-modality correlation evaluation (CMCE), the channel fusion transformer (CFT), and the selective Kernel (SK) based decoder. The MBFE module captures and integrates various modal features, while the CMCE module facilitates the fusion process between the MBFE and decoder. The CFT module selectively guides the flow of information between the MBFE and decoder and effectively utilizes skip connections from multiple layers. The SK module computes the significance of each channel using global pooling operations and applies weights to the input feature maps to improve the models recognition of important features.Main results.The proposed MAS-Net achieved a dice coefficient of 93.08% on IVD3Seg and 93.22% on DualModalDisc dataset, outperforming the current state-of-the-art network, accurately segmenting the LIDs, and generating a 3D model that can precisely display the LIDs.Significance.MAS-Net automates the diagnostics process and addresses challenges faced by doctors. Simplifying and enhancing the clarity of visual representation, multi-modal MRI allows for better information complementation and LIDs segmentation. By successfully integrating data from various modalities, the accuracy of LID segmentation is improved.

MLP-Res-Unet：MLPs and residual blocks-based U-shaped network intervertebral disc segmentation of multi-modal MR spine images.

Intervertebral disc degeneration (IVD) is now the most prevalent disease in the world; thus, precise intervertebral disc segmentation is essential for the assessment and diagnosis of spinal diseases. Multi-modal magnetic resonance (MR) imaging is more multi-dimensional and thorough than unimodal imaging. However, manual segmentation of multi-modal MRI not only imposes a huge burden on physicians but also has a high error rate. In this study, we propose a new method that can efficiently and accurately segment intervertebral discs from multi-modal MR spine images, providing a reproducible usage scheme for the diagnosis of spinal disorders. We suggest a network structure called MLP-Res-Unet that reduces the amount of computational load and the number of parameters while maintaining performance. Our contribution is two-fold. First, a medical image segmentation network that fuses residual blocks and a multilayer perceptron (MLP) is proposed. Secondly, we design a new deep supervised method and pass the features extracted from the encoder to the decoder through the residual path to achieve a new full-scale residual connection. We evaluate the network on the MICCAI-2018 IVD dataset and obtain Dice similarity coefficient equal to 94.77 (%) and Jaccard coefficient equal to 84.74 (%), while we reduce the amount of parameters by a factor of 3.9 and computation by a factor of 2.4 compared to the IVD-Net. Experiments show that MLP-Res-Unet improves segmentation performance and creates a simpler model structure while reducing the number of parameters and computation.

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.

SSCK-Net: Spine segmentation in MRI based on cross attention and key-points recognition-assisted learner

Multiple spine segmentation of vertebrae and intervertebral discs for magnetic resonance images (MRI) plays a significant role in various spinal diseases diagnosis and treatments of spine disorder. However, the inherent complexity and characteristics of the spine pose challenges in balancing the inter-class similarity and intra-class variety of spine, improving generalization ability, learning rate, and accuracy. To address the above issues, a spine segmentation in MRI based on cross attention and key-points recognition-assisted learner (SSCK-Net) is proposed. Firstly, a multi-channel cross attention (MCCA) mechanism is put forward to generate a comprehensive description of the spine by fusing complementary inter-class feature and intra-class feature. Secondly, a key-points recognition-assisted learner (KRAL) is designed, which includes mixed-supervision recognition-assisted label fusion (RALF), to reduce dependence on a single dataset and improve the generalization ability of the network. Subsequently, adaptive transformer (AT) is presented to perform parallel computation through selective kernel fusion, nonlinear activation function with simple inverse and rescaling layer normalization, which reduces training time and prevents performance degradation due to long-term dependence. Finally, a feature enhances unit (FEU) is proposed to fuse the key point features and the segmented features while constraining the key-point features to the fused features again by the refine unit. Our experiments on T2-weighted volumetric MRI show that SSCK-Net achieves impressive performance with a mean Dice similarity coefficient (DSC) 96.12% for 5 vertebral bodies and 95.07% for 5 intervertebral discs on public datasets, outperforming state-of-the-art techniques.

Semi-supervised hybrid spine network for segmentation of spine MR images.

Automatic segmentation of vertebral bodies (VBs) and intervertebral discs (IVDs) in 3D magnetic resonance (MR) images is vital in diagnosing and treating spinal diseases. However, segmenting the VBs and IVDs simultaneously is not trivial. Moreover, problems exist, including blurry segmentation caused by anisotropy resolution, high computational cost, inter-class similarity and intra-class variability, and data imbalances. We proposed a two-stage algorithm, named semi-supervised hybrid spine network (SSHSNet), to address these problems by achieving accurate simultaneous VB and IVD segmentation. In the first stage, we constructed a 2D semi-supervised DeepLabv3+ by using cross pseudo supervision to obtain intra-slice features and coarse segmentation. In the second stage, a 3D full-resolution patch-based DeepLabv3+ was built. This model can be used to extract inter-slice information and combine the coarse segmentation and intra-slice features provided from the first stage. Moreover, a cross tri-attention module was applied to compensate for the loss of inter-slice and intra-slice information separately generated from 2D and 3D networks, thereby improving feature representation ability and achieving satisfactory segmentation results. The proposed SSHSNet was validated on a publicly available spine MR image dataset, and remarkable segmentation performance was achieved. Moreover, results show that the proposed method has great potential in dealing with the data imbalance problem. Based on previous reports, few studies have incorporated a semi-supervised learning strategy with a cross attention mechanism for spine segmentation. Therefore, the proposed method may provide a useful tool for spine segmentation and aid clinically in spinal disease diagnoses and treatments. Codes are publicly available at: https://github.com/Meiyan88/SSHSNet.

ICUnet++: an Inception-CBAM network based on Unet++ for MR spine image segmentation.

In recent years, more attention paid to the spine caused by related diseases, spinal parsing (the multi-class segmentation of vertebrae and intervertebral disc) is an important part of the diagnosis and treatment of various spinal diseases. The more accurate the segmentation of medical images, the more convenient and quick the clinicians can evaluate and diagnose spinal diseases. Traditional medical image segmentation is often time consuming and energy consuming. In this paper, an efficient and novel automatic segmentation network model for MR spine images is designed. The proposed Inception-CBAM Unet++ (ICUnet++) model replaces the initial module with the Inception structure in the encoder-decoder stage base on Unet++ , which uses the parallel connection of multiple convolution kernels to obtain the features of different receptive fields during in the feature extraction. According to the characteristics of the attention mechanism, Attention Gate module and CBAM module are used in the network to make the attention coefficient highlight the characteristics of the local area. To evaluate the segmentation performance of network model, four evaluation metrics, namely intersection over union (IoU), dice similarity coefficient(DSC), true positive rate(TPR), positive predictive value(PPV) are used in the study. The published SpineSagT2Wdataset3 spinal MRI dataset is used during the experiments. In the experiment results, IoU reaches 83.16%, DSC is 90.32%, TPR is 90.40%, and PPV is 90.52%. It can be seen that the segmentation indicators have been significantly improved, which reflects the effectiveness of the model.

Lumbar intervertebral disc segmentation for computer modeling and simulation

Background and objectiveThe present work had as its main objective the development of a method for localizing and automatically segmenting lumbar intervertebral discs (IVD) in 3D from magnetic resonance imaging (MRI), with the goal of supporting the generation of finite element (FE) models from actual lumbar spine anatomy, by providing accurate and personalized information on the shape of the patient's IVD. The extension of the method to allow performing separate segmentations of the IVD's two main structures – annulus fibrosus (AF) and nucleus pulposus (NP) – as well as automatically detecting degenerated IVD where this distinction is no longer possible was also an objective of the work. MethodsThe method presented here evolves from 2D segmentations in the sagittal profile using Gabor filters towards 3D segmentations. It works by detecting the spine curves and intensity regions corresponding to IVD. As so, the 2D method from Zhu et al. (2013) was partially implemented, modified and adapted to 3D use, and then tested with eight spines from two separated online datasets. The 3D adaptation was achieved by using vertebral body segmentation masks to approximate the shape of the vertebrae and to adjust the spine curves accordingly. ResultsThe method showed average values of 85%, 83% and 96% for the Dice coefficient, sensitivity and specificity, respectively. The method correctly identified 65 of 68 (96%) IVD as either healthy or degenerated. The method's Dice coefficient is within the range of existing 3D IVD segmentation methods in the literature (81–92%). The method took on average 6–7 s to perform a full 3D segmentation, which is well within the range of the existing methods (2 s – 19 min). ConclusionsThe developed method can be used to generate accurate 3D models of the IVD based on MRI, with AF/NP distinction and detection of marked degeneration by comparing each IVD with the remaining spine levels. Further work shall improve the method towards distinguishing between specific levels of degeneration for clinically oriented FE modeling.

Grayscale self-adjusting network with weak feature enhancement for 3D lumbar anatomy segmentation.

The automatic segmentation of lumbar anatomy is a fundamental problem for the diagnosis and treatment of lumbar disease. The recent development of deep learning techniques has led to remarkable progress in this task, including the possible segmentation of nerve roots, intervertebral discs, and dural sac in a single step. Despite these advances, lumbar anatomy segmentation remains a challenging problem due to the weak contrast and noise of input images, as well as the variability of intensities and size in lumbar structures across different subjects. To overcome these challenges, we propose a coarse-to-fine deep neural network framework for lumbar anatomy segmentation, which obtains a more accurate segmentation using two strategies. First, a progressive refinement process is employed to correct low-confidence regions by enhancing the feature representation in these regions. Second, a grayscale self-adjusting network (GSA-Net) is proposed to optimize the distribution of intensities dynamically. Experiments on datasets comprised of 3D computed tomography (CT) and magnetic resonance (MR) images show the advantage of our method over current segmentation approaches and its potential for diagnosing and lumbar disease treatment.

Residual-atrous attention network for lumbosacral plexus segmentation with MR image

Accurate segmentation of the lumbosacral plexus is a crucial step for diagnosis and analysis of nerve damage in clinical. Due to the extremely low contrast and complicated structure around the lumbosacral plexus, it has been remaining a challenging task to effectively segment the lumbosacral plexus from spinal MR images. Even though several deep learning methods for spine segmentation have been developed, most of them only pay attention to the segmentation of vertebral bodies and intervertebral discs rather than nerves. To solve these problems, in this paper, we propose a residual-atrous attention network (RA2-Net) for lumbosacral plexus segmentation with MR images. Specifically, the RA2-Net consists of three main parts, (1) the atrous encoder module is employed to learn multi-scale contextual features from MR images in the encoder, (2) the residual skip connection operation is used to integrate the features with high-resolution spatial details in the encoder and the high-level contextual features in the decoder, and (3) the scale attention block is proposed for fusing the multi-scale high-level features in the decoder. We perform our proposed RA2-Net for the lumbosacral plexus segmentation on the collected spinal MRI dataset with 10 patients (a total of 236 MRI scans). Extensive experiments demonstrate that our RA2-Net achieves better performance in lumbosacral plexus segmentation with MR images when compared with several state-of-the-art methods.

Spine Segmentation with Multi-view GCN and Boundary Constraint.

Multi-class segmentation of vertebrae and inter-vertebral discs (IVDs) is crucial for the diagnosis and treatment of spinal diseases. However, it is still a challenge due to similarities between neighboring vertebrae of a subject and differences among the IVDs from different subjects. In this paper, we propose a novel spine segmentation framework to achieve automatic segmentation of vertebrae and IVDs in MR images. The core component of the new framework is a Multi-View GCN (MVGCN), which utilizes multi-view features and graph convolutional network (GCN) to reason about the relations of vertebrae and IVDs. We additionally use a boundary constraint for better segmentation of the boundary between vertebrae and IVDs. We test our method on a public spine dataset of 172 MR volumetric images for the vertebrae and IVDs segmentation. The experimental results demonstrate the efficacy of our method. Code and models of our method will be available in the future.