Segmentation Of Magnetic Resonance Images Research Articles

Brain tumor segmentation in MRI with multi-modality spatial information enhancement and boundary shape correction

Brain tumor segmentation is currently of a priori guiding significance in medical research and clinical diagnosis. Brain tumor segmentation techniques can accurately partition different tumor areas on multi-modality images captured by magnetic resonance imaging (MRI). Due to the unpredictable pathological process of brain tumor generation and growth, brain tumor images often show irregular shapes and uneven internal gray levels. Existing neural network-based segmentation methods with an encoding/decoding structure can perform image segmentation to some extent. However, they ignore issues such as differences in multi-modality information, loss of spatial information, and under-utilization of boundary information, thereby limiting the further improvement of segmentation accuracy. This paper proposes a multimodal spatial information enhancement and boundary shape correction method consisting of a modality information extraction (MIE) module, a spatial information enhancement (SIE) module, and a boundary shape correction (BSC) module. The above three modules act on the input, backbone, and loss functions of deep convolutional networks (DCNN), respectively, and compose an end-to-end 3D brain tumor segmentation model. The three proposed modules can solve the low utilization rate of effective modality information, the insufficient spatial information acquisition ability, and the improper segmentation of key boundary positions can be solved. The proposed method was validated on BraTS2017, 2018, and 2019 datasets. Comparative experimental results confirmed the effectiveness and superiority of the proposed method over state-of-the-art segmentation methods.

Brain MR image segmentation for tumor detection based on Riesz probability distributions

ABSTRACT This research introduces a new approach using the Riesz mixture model for medical image segmentation, specifically for diagnosing and treating brain tumors. We developed a novel technique for pixel classification based on the Riesz distribution, which is generated using an extended Bartlett decomposition. Our work is pioneering, as there are no existing studies addressing this issue in the literature. We aim to demonstrate the effectiveness of this distribution for brain image segmentation. We used the Expectation-Maximization algorithm to estimate the mixture parameters. To validate our segmentation algorithm, we conducted a comparative study with a recent method based on the Wishart distribution using Matlab software. Experiments with the Riesz mixture model showed that our method produces more intuitive results with a recognition rate of 94.52%. These results confirm the reliability of our method in detecting tumors using both synthetic and real brain images.

Automatic segmentation of tumour and organs at risk in 3D MRI for cervical cancer radiation therapy with anatomical variations.

Cervical cancer is a common cancer in women globally, with treatment usually involving radiation therapy (RT). Accurate segmentation for the tumour site and organ-at-risks (OARs) could assist in the reduction of treatment side effects and improve treatment planning efficiency. Cervical cancer Magnetic Resonance Imaging (MRI) segmentation is challenging due to a limited amount of training data available and large inter- and intra- patient shape variation for OARs. The proposed Masked-Net consists of a masked encoder within the 3D U-Net to account for the large shape variation within the dataset, with additional dilated layers added to improve segmentation performance. A new loss function was introduced to consider the bounding box loss during training with the proposed Masked-Net. Transfer learning from a male pelvis MRI data with a similar field of view was included. The approaches were compared to the 3D U-Net which was widely used in MRI image segmentation. The data used consisted of 52 volumes obtained from 23 patients with stage IB to IVB cervical cancer across a maximum of 7 weeks of RT with manually contoured labels including the bladder, cervix, gross tumour volume, uterus and rectum. The model was trained and tested with a 5-fold cross validation. Outcomes were evaluated based on the Dice Similarity Coefficients (DSC), the Hausdorff Distance (HD) and the Mean Surface Distance (MSD). The proposed method accounted for the small dataset, large variations in OAR shape and tumour sizes with an average DSC, HD and MSD for all anatomical structures of 0.790, 30.19mm and 3.15mm respectively.

Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation in T1 Images.

Magnetic Resonance Imaging (MRI) plays an important role in neurology, particularly in the precise segmentation of brain tissues. Accurate segmentation is crucial for diagnosing brain injuries and neurodegenerative conditions. We introduce an Enhanced Spatial Fuzzy C-means (esFCM) algorithm for 3D T1 MRI segmentation to three tissues, i.e. White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). The esFCM employs a weighted least square algorithm utilizing the Structural Similarity Index (SSIM) for polynomial bias field correction. It also takes advantage of the information from the membership function of the last iteration to compute neighborhood impact. This strategic refinement enhances the algorithm's adaptability to complex image structures, effectively addressing challenges such as intensity irregularities and contributing to heightened segmentation accuracy. We compare the segmentation accuracy of esFCM against four variants of FCM, Gaussian Mixture Model (GMM) and FSL and ANTs algorithms using four various dataset, employing three measurement criteria. Comparative assessments underscore esFCM's superior performance, particularly in scenarios involving added noise and bias fields.The obtained results emphasize the significant potential of the proposed method in the segmentation of MRI images.

Imaging segmentation mechanism for rectal tumors using improved U-Net

ObjectiveIn radiation therapy, cancerous region segmentation in magnetic resonance images (MRI) is a critical step. For rectal cancer, the automatic segmentation of rectal tumors from an MRI is a great challenge. There are two main shortcomings in existing deep learning-based methods that lead to incorrect segmentation: 1) there are many organs surrounding the rectum, and the shape of some organs is similar to that of rectal tumors; 2) high-level features extracted by conventional neural networks often do not contain enough high-resolution information. Therefore, an improved U-Net segmentation network based on attention mechanisms is proposed to replace the traditional U-Net network.MethodsThe overall framework of the proposed method is based on traditional U-Net. A ResNeSt module was added to extract the overall features, and a shape module was added after the encoder layer. We then combined the outputs of the shape module and the decoder to obtain the results. Moreover, the model used different types of attention mechanisms, so that the network learned information to improve segmentation accuracy.ResultsWe validated the effectiveness of the proposed method using 3773 2D MRI datasets from 304 patients. The results showed that the proposed method achieved 0.987, 0.946, 0.897, and 0.899 for Dice, MPA, MioU, and FWIoU, respectively; these values are significantly better than those of other existing methods.ConclusionDue to time savings, the proposed method can help radiologists segment rectal tumors effectively and enable them to focus on patients whose cancerous regions are difficult for the network to segment.SignificanceThe proposed method can help doctors segment rectal tumors, thereby ensuring good diagnostic quality and accuracy.

An Intuitionistic Fuzzy C-Means and Local Information-Based DCT Filtering for Fast Brain MRI Segmentation.

Structural and photometric anomalies in the brain magnetic resonance images (MRIs) affect the segmentation performance. Moreover, a sudden change in intensity between two boundaries of the brain tissues makes it prone to data uncertainty, resulting in the misclassification of the pixels lying near the cluster boundaries. The discrete cosine transform (DCT) domain-based filtering is an effective way to deal with structural and photometric anomalies, while the intuitionistic fuzzy C-means (IFCM) clustering can handle the uncertainty using the intuitionistic fuzzy set (IFS) theory. In this background, we propose two novel approaches, namely, the DCT-based intuitionistic fuzzy C-means (DCT-IFCM) and the DCT-based local information IFCM (DCT-LIFCM), which effectively deal with the Rician and Gaussian noises and also handle the data uncertainty problem to provide high segmentation accuracy. The DCT-IFCM approach performs the histogram-based segmentation, while the DCT-LIFCM uses the pixel-wise computation to include the spatial information. Although the DCT-LIFCM delivers slightly better performance than the DCT-IFCM, the latter is very fast in providing equally high segmentation accuracy. An exhaustive performance analysis is provided to demonstrate the superior performance of the proposed algorithms compared with the state-of-the-art algorithms, including those based on the DCT-based filtering approach and the IFS theory.

Hybrid dual mean-teacher network with double-uncertainty guidance for semi-supervised segmentation of magnetic resonance images

Semi-supervised learning has made significant progress in medical image segmentation. However, existing methods primarily utilize information from a single dimensionality, resulting in sub-optimal performance on challenging magnetic resonance imaging (MRI) data with multiple segmentation objects and anisotropic resolution. To address this issue, we present a Hybrid Dual Mean-Teacher (HD-Teacher) model with hybrid, semi-supervised, and multi-task learning to achieve effective semi-supervised segmentation. HD-Teacher employs a 2D and a 3D mean-teacher network to produce segmentation labels and signed distance fields from the hybrid information captured in both dimensionalities. This hybrid mechanism allows HD-Teacher to utilize features from 2D, 3D, or both dimensions as needed. Outputs from 2D and 3D teacher models are dynamically combined based on confidence scores, forming a single hybrid prediction with estimated uncertainty. We propose a hybrid regularization module to encourage both student models to produce results close to the uncertainty-weighted hybrid prediction to further improve their feature extraction capability. Extensive experiments of binary and multi-class segmentation conducted on three MRI datasets demonstrated that the proposed framework could (1) significantly outperform state-of-the-art semi-supervised methods (2) surpass a fully-supervised VNet trained on substantially more annotated data, and (3) perform on par with human raters on muscle and bone segmentation task. Code will be available at https://github.com/ThisGame42/Hybrid-Teacher.

Automatic design of W-operators using membership functions: a case study in brain MRI segmentation

AbstractA W-operator is an image transformation that is locally defined inside a window W, invariant to translations. The automatic design of the W-operators consists of the design of functions, whose domain is a set of patterns or vectors obtained by translating a window through training images and the output of each vector is a class or label. The main difficulty to consider when designing W-operators is the generalization problem that occurs due to lack of training images. In this work, we propose the use of membership functions to solve the generalization problem in gray level images. Membership functions are defined from the training images to model regions that are often inaccurate due to ambiguous gray levels in the images. This proposal was applied to brain magnetic resonance image segmentation to test its performance in a field of interest in biomedical images. The experiments were carried out with different numbers of training and test images, windows sizes of $$3\times 3$$ 3 × 3 , $$5\times 5$$ 5 × 5 , $$7\times 7$$ 7 × 7 , $$11\times 11$$ 11 × 11 , and $$15\times 15$$ 15 × 15 , and images with noise levels at 0, 1, 3, 5, 7, and 9$$\%$$ % . To calculate the performance of each designed W-operator, the classification error, sensitivity, and specificity were used. From the experimental results, it was concluded that the best performance is achieved with a window of size $$3\times 3$$ 3 × 3 . In images with noise levels from 1 to 5$$\%$$ % , the classification error is less than 4$$\%$$ % and the sensitivity and specificity are greater than 94 and 98$$\%$$ % , respectively.

Light3DHS: A lightweight 3D hippocampus segmentation method using multiscale convolution attention and vision transformer

The morphological analysis and volume measurement of the hippocampus are crucial to the study of many brain diseases. Therefore, an accurate hippocampal segmentation method is beneficial for the development of clinical research in brain diseases. U-Net and its variants have become prevalent in hippocampus segmentation of Magnetic Resonance Imaging (MRI) due to their effectiveness, and the architecture based on Transformer has also received some attention. However, some existing methods focus too much on the shape and volume of the hippocampus rather than its spatial information, and the extracted information is independent of each other, ignoring the correlation between local and global features. In addition, many methods cannot be effectively applied to practical medical image segmentation due to many parameters and high computational complexity. To this end, we combined the advantages of CNNs and ViTs (Vision Transformer) and proposed a simple and lightweight model: Light3DHS for the segmentation of the 3D hippocampus. In order to obtain richer local contextual features, the encoder first utilizes a multi-scale convolutional attention module (MCA) to learn the spatial information of the hippocampus. Considering the importance of local features and global semantics for 3D segmentation, we used a lightweight ViT to learn high-level features of scale invariance and further fuse local-to-global representation. To evaluate the effectiveness of encoder feature representation, we designed three decoders of different complexity to generate segmentation maps. Experiments on three common hippocampal datasets demonstrate that the network achieves more accurate hippocampus segmentation with fewer parameters. Light3DHS performs better than other state-of-the-art algorithms.

CT-γ-Net: A Hybrid Model based on Convolutional Encoder-Decoder and Transformer Encoder for Brain Tumor Localization

Brain Tumor is a life-threatening disease, and its early diagnosis can save human life. Computer-Aided Brain Tumor Segmentation or Localization in Magnetic Resonance Imaging (MRI) images have emerged as pivotal approaches for expediting the disease diagnosis process. In the past few decades, various researchers combined the strengths of Convolutional Networks and Transformer to perform Brain Tumor Segmentation. However, these models require a large number of trainable weights parameters, and there is still scope for performance improvement in them. To bridge these research gaps, this paper proposes a novel hybrid model named “CT-γ-Net” for effective and efficient Brain Tumor Localization. The proposed CT-γ-Net model follows an Encoder-Decoder structure in which the Convolutional Encoder (CE) and Transformer Encoder (TE) are used for encoding, whereas the Convolutional Decoder (CD) is utilized for decoding the combined output of CE and TE to generate the segmentation masks. In CE and CD components of CT-γ-Net model conventional convolutional layers are replaced by Depth-Wise Separable convolutional layers, as these layers significantly reduce trainable weights parameters. The proposed model achieves 95.5% MeanIoU, 94.82% Dice Score, and 99.24% Pixel Accuracy on publicly available dataset named The Cancer Imaging Archive (TCIA). These experimental results demonstrate that the CT-γ-Net model outperformed other state-of-the-art research works, despite using roughly 28% fewer trainable weights parameters. Hence, the proposed model’s lightweight nature and its high performance, makes it a suitable candidate for deployment on mobile devices, facilitating the precise localization of brain tumor regions in MRI images.

Post mortem analysis of hepatic volume and lipid content by magnetic resonance imaging and spectroscopy in fixed murine neonates.

Maternal obesity and hyperglycemia are linked to an elevated risk for obesity, diabetes, and steatotic liver disease in the adult offspring. To establish and validate a noninvasive workflow for perinatal metabolic phenotyping, fixed neonates of common mouse strains were analyzed postmortem via magnetic resonance imaging (MRI)/magnetic resonance spectroscopy (MRS) to assess liver volume and hepatic lipid (HL) content. The key advantage of nondestructive MRI/MRS analysis is the possibility of further tissue analyses, such as immunohistochemistry, RNA extraction, and even proteomics, maximizing the data that can be gained per individual and therefore facilitating comprehensive correlation analyses. This study employed an MRI and 1H-MRS workflow to measure liver volume and HL content in 65 paraformaldehyde-fixed murine neonates at 11.7 T. Liver volume was obtained using semiautomatic segmentation of MRI acquired by a RARE sequence with 0.5-mm slice thickness. HL content was measured by a STEAM sequence, applied with and without water suppression. T1 and T2 relaxation times of lipids and water were measured for respective correction of signal intensity. The HL content, given as CH2/(CH2+ H2O), was calculated, and the intrasession repeatability of the method was tested. The established workflow yielded robust results with a variation of ~3% in repeated measurements for HL content determination. HL content measurements were further validated by correlation analysis with biochemically assessed triglyceride contents (R2= 0.795) that were measured in littermates. In addition, image quality also allowed quantification of subcutaneous adipose tissue and stomach diameter. The highest HL content was measured in C57Bl/6N (4.2%) and the largest liver volume and stomach diameter in CBA (53.1 mm3 and 6.73 mm) and NMRI (51.4 mm3 and 5.96 mm) neonates, which also had the most subcutaneous adipose tissue. The observed effects were independent of sex and litter size. In conclusion, we have successfully tested and validated a robust MRI/MRS workflow that allows assessment of morphology and HL content and further enables paraformaldehyde-fixed tissue-compatible subsequent analyses in murine neonates.

Introducing the Concept of Sanctuary Sites in Ischemic Stroke.

The topography of arterial territories has been defined using digital maps of supratentorial infarcts. Regions with a high probability of infarction (Pi) exist in the deep compartment due to a paucity of collaterals. However, less attention has been given to regions with a low Pi. Using published digital maps, patients with cortical stroke and documented vessel occlusion were included. Infarcts from T2-weighted magnetic resonance images were segmented and registered onto a standard brain template (Montreal Neurological Institute 152). Segmented magnetic resonance images were averaged to yield the Pi at a voxel level. The overall Pi for the combined arterial territories was calculated to ensure that Pi was in the range of 0 to 1. Sanctuary sites were identified as regions with Pi <0.1. There were 154 patients (63% men; median age, 69 years; and interquartile range, 57-78 years). The magnetic resonance imaging scan used for segmentation was performed at a median interval of 35 (interquartile range, 3-66) days after stroke onset. Sanctuary sites were present in the frontal (gyrus rectus, the paracentral lobule, and orbitofrontal and precentral gyrus), parietal (postcentral, supramarginal, and angular gyrus, superior and inferior parietal lobule, and precuneus and posterior cingulate), and occipital cortex (cuneus, middle, and superior occipital gyrus). We propose that following vessel occlusion, there are cortical regions with a low Pi, which we termed sanctuary sites. The anatomic basis for this observation is the compensatory capacity of leptomeningeal collaterals.

BUA-Net: Boundary and uncertainty-aware attention network for lumbar multi-region magnetic resonance imaging segmentation

Background and Objective:Multi-region segmentation by axial lumbar magnetic resonance imaging (MRI) is important for the diagnosis of lumbar spinal stenosis (LSS) and lumbar disc herniation (LDH) disease. Deep learning-based methods have been widely used in this field, but the segmentation task is still challenging due to intra-class grayscale unevenness, inter-class low contrast and unclear boundaries. Methods:To solve the above problems, we propose a novel boundary and uncertainty-aware attention network (BUA-Net). It is structured around a single encoder and two decoders, each dedicated to either multi-region or boundary segmentation. We use an uncertainty-aware attention (UA) module to extract regions with large segmentation uncertainty and generate an attention mask. The learning and supervisory capabilities of the model are enhanced by a fine seg (FS) module and an attention supervision loss (ASL) in the boundaries and blurred regions. Moreover, a multi-scale spatial channel attention (MSCA) module is proposed that refines the high-level features without increasing any layers. Results:The results of the BUA-Net experiments were tested against seven networks and showed a 0.26% improvement in Dice scores, a 0.86% improvement in precision, and a 0.2% improvement in recall. In metrics related to boundary segmentation effectiveness, our method nearly matches the state-of-the-art in the ASD metric, surpasses it by 2.32 in the 95HD metric, and leads by 2.04, 2.68, and 1.22 in the IVD, TS, and PE categories, respectively. Conclusions:The experimental results demonstrate that our proposed method outperforms other state-of-the-art methods and shows excellent segmentation ability in the boundaries and blurred regions.

A review of self-supervised, generative, and few-shot deep learning methods for data-limited magnetic resonance imaging segmentation.

Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent of advanced deep learning frameworks, fully automated and accurate MRI segmentation is advancing. Traditional supervised deep learning techniques have advanced tremendously, reaching clinical-level accuracy in the field of segmentation. However, these algorithms still require a large amount of annotated data, which is oftentimes unavailable or impractical. One way to circumvent this issue is to utilize algorithms that exploit a limited amount of labeled data. This paper aims to review such state-of-the-art algorithms that use a limited number of annotated samples. We explain the fundamental principles of self-supervised learning, generative models, few-shot learning, and semi-supervised learning and summarize their applications in cardiac, abdomen, and brain MRI segmentation. Throughout this review, we highlight algorithms that can be employed based on the quantity of annotated data available. We also present a comprehensive list of notable publicly available MRI segmentation datasets. To conclude, we discuss possible future directions of the field-including emerging algorithms, such as contrastive language-image pretraining, and potential combinations across the methods discussed-that can further increase the efficacy of image segmentation with limited labels.

Post-Operative Brain MRI Resection Cavity Segmentation Model and Follow-Up Treatment Assistance

Post-operative brain magnetic resonance imaging (MRI) segmentation is inherently challenging due to the diverse patterns in brain tissue, which makes it difficult to accurately identify resected areas. Therefore, there is a crucial need for a precise segmentation model. Due to the scarcity of post-operative brain MRI scans, it is not feasible to use complex models that require a large amount of training data. This paper introduces an innovative approach for accurately segmenting and quantifying post-operative brain resection cavities in MRI scans. The proposed model, named Attention-Enhanced VGG-U-Net, integrates VGG16 initial weights in the encoder section and incorporates a self-attention module in the decoder, offering improved accuracy for postoperative brain MRI segmentation. The attention mechanism enhances its accuracy by concentrating on a specific area of interest. The VGG16 model is comparatively lightweight, has pre-trained weights, and allows the model to extract incredibly detailed information from the input. The model is trained on publicly available post-operative brain MRI data and achieved a Dice coefficient value of 0.893. The model is then assessed using a clinical dataset of postoperative brain MRIs. The model facilitates the quantification of the resected regions and enables comparisons with each brain region based on pre-operative images. The capabilities of the model assist radiologists in evaluating surgical success and directing follow-up procedures.

Brain magnetic resonance images segmentation via improved mixtures of factor analyzers based on dynamic co-clustering

The main goal of this paper is to attain accurate and automatic detection of brain tumors in gray magnetic resonance images using reducing the local dimensions. We propose a novel model called Improved Mixtures of Factor Analyzers based on Dynamic Co-Clustering (IMFADCC) for the detection and localization of brain tumors. After image preprocessing and enhancement, the optimal numbers of row clusters and column clusters corresponding to each row cluster, alongside other model parameters, are determined concurrently based on the size of the tumor by our model. Then, using the optimal values obtained, the image is co-clustered by simultaneously clustering rows and columns until the block containing the tumor is identified. The output image is divided into a certain number of blocks, one of which contains the tumor. For post-processing, the identified block is binarized using minimum error thresholding. The proposed model accurately detects and locates the block binarized within the original image while considering the remainder of the image as background. The effectiveness of the proposed method was assessed by evaluating its accuracy, Dice, intersection on union, geometric mean, receiver operating characteristic curve, specificity, and sensitivity criteria on the BraTS2018, BraTS2019, and BraTS2020 datasets. The results show that our method has significant performance and is highly accurate in detecting tumors of different sizes in complex and high-size images. Moreover, our method outperforms the existing diagnostic methods.

Magnetic Resonance Images Segmentation of Multifidus based on Dense-unet and Superpixel.

Lumbar disc herniation (LDH) is a common clinical condition causing lower back and leg pain. Accurate segmentation of the lumbar discs is crucial for assessing and diagnosing LDH. Magnetic resonance imaging (MRI) can reveal the condition of articular cartilage. However, manual segmentation of MRI images is burdensome for physicians and needs to be more efficient. In this study, we propose a method that combines UNet and superpixel segmentation to address the problem of loss of detailed information in the feature extraction phase, leading to poor segmentation results at object edges. The aim is to provide a reproducible solution for diagnosing patients with lumbar disc herniation. We suggest using the network structure of UNet. Firstly, dense blocks are inserted into the UNet network, and training is performed using the Swish activation function. The Dense-UNet model extracts semantic features from the images and obtains rough semantic segmentation results. Then, an adaptive-scale superpixel segmentation algorithm is applied to segment the input images into superpixel images. Finally, high-level abstract semantic features are fused with the detailed information of the superpixels to obtain edge-optimized semantic segmentation results. Evaluation of a private dataset of multifidus muscles in magnetic resonance images demonstrates that compared to other segmentation algorithms, this algorithm exhibits better semantic segmentation performance in detailed areas such as object edges. Compared to UNet, it achieves a 9.5% improvement in the Dice Similarity Coefficient (DSC) and an 11.3% improvement in the Jaccard Index (JAC). The experimental results indicate that this algorithm improves segmentation performance while reducing computational complexity.

Improved UNet‐based magnetic resonance imaging segmentation of demyelinating diseases with small lesion regions

AbstractAccurate magnetic resonance imaging (MRI) segmentation plays a critical role in the diagnosis and treatment of demyelinating diseases. But the existing automatic segmentation methods are not suitable for the segmentation of demyelinating lesions with small lesion size, highly diffuse edges and complex boundary shapes. An improved model is proposed for demyelinating diseases MRI segmentation based on the U‐shaped structure convolution neural networks (UNet). A context information weighting fusion (CIWF) module and a modified channel attention (MCA) module are developed and embedded in UNet to address the small lesion region and diffuse edge issues. The CIWF module can dynamically screen and fuse shallow and deep features at different stages, making the model pay more attention to small lesions. The MCA module enables the model to learn diverse features by adding weights to the channel, which helps in diffuse edge segmentation. Comparisons with many existing methods on real‐world demyelinating disease MRI segmentation dataset show that our method achieve the highest Dice metric.

Lumbar spine segmentation in MR images: a dataset and a public benchmark

This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain and was collected from four different hospitals. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images. The algorithm provided an initial segmentation, which was subsequently reviewed, manually corrected, and added to the training data. We provide reference performance values for this baseline algorithm and nnU-Net, which performed comparably. Performance values were computed on a sequestered set of 39 studies with 97 series, which were additionally used to set up a continuous segmentation challenge that allows for a fair comparison of different segmentation algorithms. This study may encourage wider collaboration in the field of spine segmentation and improve the diagnostic value of lumbar spine MRI.

RIBM3DU‐Net: Glioma tumour substructures segmentation in magnetic resonance images using residual‐inception block with modified 3D U‐Net architecture

AbstractGlioma brain tumour is one of the life‐threatening diseases in the world. Tumour substructure segmentation by physicians is a time‐consuming task with the magnetic resonance imaging (MRI) technique due to the size of clinical data. An automatic and well‐trained method is essential to detect and segment the tumour which increase the survival of the patients. The proposed work aims to produce high accuracy on glioma substructures segmentation with less computation time using deep learning. From the literature survey, the following challenges are found: (i) computing complex spatial boundaries between normal and tumour tissues, (ii) feature reduction and (iii) overfitting problems. Hence, we proposed a fully automatic glioma tumour segmentation using a residual‐inception block (RIB) with a modified 3D U‐Net (RIBM3DU‐Net). It includes three phases: pre‐processing, modified 3D U‐Net segmentation and post‐processing. From the results, RIB with U‐Net enhances the segmentation accuracy. GPU parallel architecture reduces the computation time while training and testing. For quantitative analysis, comprehensive experiment results were computed and compared with state‐of‐the‐art methods. It achieves better Dice scores on enhancing tumour, tumour core and complete tumour of 87%, 87% and 94%, respectively. GPU speedup folds yield up to 48× when compared with CPU. Quantitatively, 3D glioma volume is rendered from the obtained segmented results and estimated using the Cavalieri estimator.