Automatic Segmentation Method Research Articles

Detection of Road Crack Images Based on Multistage Feature Fusion and a Texture Awareness Method

Structural health monitoring for roads is an important task that supports inspection of transportation infrastructure. This paper explores deep learning techniques for crack detection in road images and proposes an automatic pixel-level semantic road crack image segmentation method based on a Swin transformer. This method employs Swin-T as the backbone network to extract feature information from crack images at various levels and utilizes the texture unit to extract the texture and edge characteristic information of cracks. The refinement attention module (RAM) and panoramic feature module (PFM) then merge these diverse features, ultimately refining the segmentation results. This method is called FetNet. We collect four public real-world datasets and conduct extensive experiments, comparing FetNet with various deep-learning methods. FetNet achieves the highest precision of 90.4%, a recall of 85.3%, an F1 score of 87.9%, and a mean intersection over union of 78.6% on the Crack500 dataset. The experimental results show that the FetNet approach surpasses other advanced models in terms of crack segmentation accuracy and exhibits excellent generalizability for use in complex scenes.

Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN

Objective. Automatic medical image segmentation is crucial for accurately isolating target tissue areas in the image from background tissues, facilitating precise diagnoses and procedures. While the proliferation of publicly available clinical datasets led to the development of deep learning-based medical image segmentation methods, a generalized, accurate, robust, and reliable approach across diverse imaging modalities remains elusive. Approach. This paper proposes a novel high-resolution parallel generative adversarial network (pGAN)-based generalized deep learning method for automatic segmentation of medical images from diverse imaging modalities. The proposed method showcases better performance and generalizability by incorporating novel components such as partial hybrid transfer learning, discrete wavelet transform (DWT)-based multilayer and multiresolution feature fusion in the encoder, and a dual mode attention gate in the decoder of the multi-resolution U-Net-based GAN. With multi-objective adversarial training loss functions including a unique reciprocal loss for enforcing cooperative learning in pGANs, it further enhances the robustness and accuracy of the segmentation map. Main results. Experimental evaluations conducted on nine diverse publicly available medical image segmentation datasets, including PhysioNet ICH, BUSI, CVC-ClinicDB, MoNuSeg, GLAS, ISIC-2018, DRIVE, Montgomery, and PROMISE12, demonstrate the proposed method’s superior performance. The proposed method achieves mean F1 scores of 79.53%, 88.68%, 82.50%, 93.25%, 90.40%, 94.19%, 81.65%, 98.48%, and 90.79%, respectively, on the above datasets, surpass state-of-the-art segmentation methods. Furthermore, our proposed method demonstrates robust multi-domain segmentation capabilities, exhibiting consistent and reliable performance. The assessment of the model’s proficiency in accurately identifying small details indicates that the high-resolution generalized medical image segmentation network (Hi-gMISnet) is more precise in segmenting even when the target area is very small. Significance. The proposed method provides robust and reliable segmentation performance on medical images, and thus it has the potential to be used in a clinical setting for the diagnosis of patients.

Algal Bed Region Segmentation Based on a ViT Adapter Using Aerial Images for Estimating CO2 Absorption Capacity

In this study, we propose a novel method for algal bed region segmentation using aerial images. Accurately determining the carbon dioxide absorption capacity of coastal algae requires measurements of algal bed regions. However, conventional manual measurement methods are resource-intensive and time-consuming, which hinders the advancement of the field. To solve these problems, we propose a novel method for automatic algal bed region segmentation using aerial images. In our method, we use an advanced semantic segmentation model, a ViT adapter, and adapt it to aerial images for algal bed region segmentation. Our method demonstrates high accuracy in identifying algal bed regions in an aerial image dataset collected from Hokkaido, Japan. The experimental results for five different ecological regions show that the mean intersection over union (mIoU) and mean F-score of our method in the validation set reach 0.787 and 0.870, the IoU and F-score for the background region are 0.957 and 0.978, and the IoU and F-score for the algal bed region are 0.616 and 0.762, respectively. In particular, the mean recognition area compared with the ground truth area annotated manually is 0.861. Our study contributes to the advancement of blue carbon assessment by introducing a novel semantic segmentation-based method for identifying algal bed regions using aerial images.

An improved attention module based on nnU-Net for segmenting primary central nervous system lymphoma (PCNSL) in MRI images1.

Accurate volumetric segmentation of primary central nervous system lymphoma (PCNSL) is essential for assessing and monitoring the tumor before radiotherapy and the treatment planning. The tedious manual segmentation leads to interindividual and intraindividual differences, while existing automatic segmentation methods cause under-segmentation of PCNSL due to the complex and multifaceted nature of the tumor. To address the challenges of small size, diffused distribution, poor inter-layer continuity on the same axis, and tendency for over-segmentation in brain MRI PCNSL segmentation, we propose an improved attention module based on nnUNet for automated segmentation. We collected 114 T1 MRI images of patients in the Huashan Hospital, Shanghai. Then randomly split the total of 114 cases into 5 distinct training and test sets for a 5-fold cross-validation. To efficiently and accurately delineate the PCNSL, we proposed an improved attention module based on nnU-Net with 3D convolutions, batch normalization, and residual attention (res-attention) to learn the tumor region information. Additionally, multi-scale dilated convolution kernels with different dilation rates were integrated to broaden the receptive field. We further used attentional feature fusion with 3D convolutions (AFF3D) to fuse the feature maps generated by multi-scale dilated convolution kernels to reduce under-segmentation. Compared to existing methods, our attention module improves the ability to distinguish diffuse and edge enhanced types of tumors; and the broadened receptive field captures tumor features of various scales and shapes more effectively, achieving a 0.9349 Dice Similarity Coefficient (DSC). Quantitative results demonstrate the effectiveness of the proposed method in segmenting the PCNSL. To our knowledge, this is the first study to introduce attention modules into deep learning for segmenting PCNSL based on brain magnetic resonance imaging (MRI), promoting the localization of PCNSL before radiotherapy.

Automated deep learning segmentation of high-resolution 7 Tesla postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases

Abstract Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high-resolution dataset of 135 postmortem human brain tissue specimens imaged at 0.3 mm3 isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We evaluate the reliability of this pipeline via overlap metrics with manual segmentation in 6 specimens, and intra-class correlation between cortical thickness measures extracted from the automatic segmentation and expert-generated reference measures in 36 specimens. We also segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter, providing a limited evaluation of accuracy. We show generalizing capabilities across whole-brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm3 and 0.16 mm3 isotropic T2*w fast low angle shot (FLASH) sequence at 7T. We report associations between localized cortical thickness and volumetric measurements across key regions, and semi-quantitative neuropathological ratings in a subset of 82 individuals with Alzheimer’s disease (AD) continuum diagnoses. Our code, Jupyter notebooks, and the containerized executables are publicly available at the project webpage (https://pulkit-khandelwal.github.io/exvivo-brain-upenn/).

Automated Segmentation of Brain Tumors in MRI: A Comprehensive Review of Deep Learning Approaches

The segmentation of tumor region from brain MRI images is challenging task. Automated approaches are necessary because manual segmentation takes a lot of effort and susceptible to inter-observer variability. This research provides an extensive analysis of deep learning methods for brain tumor segmentation from MRI data. The study begins with a summary of brain tumors and conventional segmentation techniques before doing a thorough evaluation of current developments in automated segmentation methods. We cover many deep learning architectures and their derivatives, specifically designed for brain tumor segmentation. The review also includes methods for differentiating between healthy brain tissue and aberrant tumor tissue. This paper critically examines the benefits, drawbacks, and potential applications of deep learning-based algorithms. It offers insights into current techniques and suggests future prospects for this important field of study.

Surveying image segmentation approaches in astronomy

Image segmentation plays a critical role in unlocking the mysteries of the universe, providing astronomers with a clearer perspective on celestial objects within complex astronomical images and data cubes. Manual segmentation, while traditional, is not only time-consuming but also susceptible to biases introduced by human intervention. As a result, automated segmentation methods have become essential for achieving robust and consistent results in astronomical studies. This review begins by summarizing traditional and classical segmentation methods widely used in astronomical tasks. Despite the significant improvements these methods have brought to segmentation outcomes, they fail to meet astronomers’ expectations, requiring additional human correction, further intensifying the labor-intensive nature of the segmentation process. The review then focuses on the transformative impact of machine learning, particularly deep learning, on segmentation tasks in astronomy. It introduces state-of-the-art machine learning approaches, highlighting their applications and the remarkable advancements they bring to segmentation accuracy in both astronomical images and data cubes. As the field of machine learning continues to evolve rapidly, it is anticipated that astronomers will increasingly leverage these sophisticated techniques to enhance segmentation tasks in their research projects. In essence, this review serves as a comprehensive guide to the evolution of segmentation methods in astronomy, emphasizing the transition from classical approaches to cutting-edge machine learning methodologies. We encourage astronomers to embrace these advancements, fostering a more streamlined and accurate segmentation process that aligns with the ever-expanding frontiers of astronomical exploration.

Adaptive Fusion of Deep Learning With Statistical Anatomical Knowledge for Robust Patella Segmentation From CT Images.

Kneeosteoarthritis (KOA), as a leading joint disease, can be decided by examining the shapes of patella to spot potential abnormal variations. To assist doctors in the diagnosis of KOA, a robust automatic patella segmentation method is highly demanded in clinical practice. Deep learning methods, especially convolutional neural networks (CNNs) have been widely applied to medical image segmentation in recent years. Nevertheless, poor image quality and limited data still impose challenges to segmentation via CNNs. On the other hand, statistical shape models (SSMs) can generate shape priors which give anatomically reliable segmentation to varying instances. Thus, in this work, we propose an adaptive fusion framework, explicitly combining deep neural networks and anatomical knowledge from SSM for robust patella segmentation. Our adaptive fusion framework will accordingly adjust the weight of segmentation candidates in fusion based on their segmentation performance. We also propose a voxel-wise refinement strategy to make the segmentation of CNNs more anatomically correct. Extensive experiments and thorough assessment have been conducted on various mainstream CNN backbones for patella segmentation in low-data regimes, which demonstrate that our framework can be flexibly attached to a CNN model, significantly improving its performance when labeled training data are limited and input image data are of poor quality.

AGSAM: Agent-Guided Segment Anything Model for Automatic Segmentation in Few-Shot Scenarios.

Precise medical image segmentation of regions of interest (ROIs) is crucial for accurate disease diagnosis and progression assessment. However, acquiring high-quality annotated data at the pixel level poses a significant challenge due to the resource-intensive nature of this process. This scarcity of high-quality annotated data results in few-shot scenarios, which are highly prevalent in clinical applications. To address this obstacle, this paper introduces Agent-Guided SAM (AGSAM), an innovative approach that transforms the Segment Anything Model (SAM) into a fully automated segmentation method by automating prompt generation. Capitalizing on the pre-trained feature extraction and decoding capabilities of SAM-Med2D, AGSAM circumvents the need for manual prompt engineering, ensuring adaptability across diverse segmentation methods. Furthermore, the proposed feature augmentation convolution module (FACM) enhances model accuracy by promoting stable feature representations. Experimental evaluations demonstrate AGSAM's consistent superiority over other methods across various metrics. These findings highlight AGSAM's efficacy in tackling the challenges associated with limited annotated data while achieving high-quality medical image segmentation.

Segmentation and Volumetric Analysis of Heart from Cardiac CT Images.

Cardiac CT is a valuable diagnostic tool in evaluating cardiovascular diseases. Accurate segmentation of the heart and its structures from cardiac CT and MRI images is essential for diagnosing functional abnormalities, treatment plans and cardiovascular diseases management. Accurate segmentation and quantitative assessments are still a challenge. Manual delineation of the heart from the scan images is labour-intensive, time-consuming, and error prone as it depends on the radiologist's experience. Thus, automated techniques are highly desirable as they can significantly improve the efficiency and accuracy of image analysis. This work addresses the above problems. A new, image-driven, fast, and fully automatic segmentation method was developed to segment the heart from CT images using a processing pipeline of adaptive median filter, multi-level thresholding, active contours, mathematical morphology, and the knowledge of human anatomy to delineate the regions of interest. The algorithm proposed is simple to implement and validate and requires no human intervention. The method is tested on the 'Image CHD' DICOM images (multi-centre, clinically approved single-phase de-identified images), and the results obtained were validated against the ground truths provided with the dataset. The results show an average Dice score, Jaccard score, and Hausdorff distance of 0.866, 0.776, and 33.29mm, respectively, for the segmentation of the heart's chambers, aorta, and blood vessels. The results and the ground truths were compared using Bland-Altmon plots. The heart was correctly segmented from the CT images using the proposed method. Further this segmentation technique can be used to develop AI based solutions for segmentation.

Quantitative Assessment of Tumor Contact with Neurogenic Zones and Its Effects on Survival: Insights beyond Traditional Predictors.

So far, the cellular origin of glioblastoma (GBM) needs to be determined, with prevalent theories suggesting emergence from transformed endogenous stem cells. Adult neurogenesis primarily occurs in two brain regions: the subventricular zone (SVZ) and the subgranular zone (SGZ) of the hippocampal dentate gyrus. Whether the proximity of GBM to these neurogenic niches affects patient outcome remains uncertain. Previous studies often rely on subjective assessments, limiting the reliability of those results. In this study, we assessed the impact of GBM's relationship with the cortex, SVZ and SGZ on clinical variables using fully automated segmentation methods. In 177 glioblastoma patients, we calculated optimal cutpoints of minimal distances to the SVZ and SGZ to distinguish poor from favorable survival. The impact of tumor contact with neurogenic zones on clinical parameters, such as overall survival, multifocality, MGMT promotor methylation, Ki-67 and KPS score was also examined by multivariable regression analysis, chi-square test and Mann-Whitney-U. The analysis confirmed shorter survival in tumors contacting the SVZ with an optimal cutpoint of 14 mm distance to the SVZ, separating poor from more favorable survival. In contrast, tumor contact with the SGZ did not negatively affect survival. We did not find significant correlations with multifocality or MGMT promotor methylation in tumors contacting the SVZ, as previous studies discussed. These findings suggest that the spatial relationship between GBM and neurogenic niches needs to be assessed differently. Objective measurements disprove prior assumptions, warranting further research on this topic.

Hepatic and portal vein segmentation with dual‐stream deep neural network

AbstractBackgroundLiver lesions mainly occur inside the liver parenchyma, which are difficult to locate and have complicated relationships with essential vessels. Thus, preoperative planning is crucial for the resection of liver lesions. Accurate segmentation of the hepatic and portal veins (PVs) on computed tomography (CT) images is of great importance for preoperative planning. However, manually labeling the mask of vessels is laborious and time‐consuming, and the labeling results of different clinicians are prone to inconsistencies. Hence, developing an automatic segmentation algorithm for hepatic and PVs on CT images has attracted the attention of researchers. Unfortunately, existing deep learning based automatic segmentation methods are prone to misclassifying peripheral vessels into wrong categories.PurposeThis study aims to provide a fully automatic and robust semantic segmentation algorithm for hepatic and PVs, guiding subsequent preoperative planning. In addition, to address the deficiency of the public dataset for hepatic and PV segmentation, we revise the annotations of the Medical Segmentation Decathlon (MSD) hepatic vessel segmentation dataset and add the masks of the hepatic veins (HVs) and PVs.MethodsWe proposed a structure with a dual‐stream encoder combining convolution and Transformer block, named Dual‐stream Hepatic Portal Vein segmentation Network, to extract local features and long‐distance spatial information, thereby extracting anatomical information of hepatic and portal vein, avoiding misdivisions of adjacent peripheral vessels. Besides, a multi‐scale feature fusion block based on dilated convolution is proposed to extract multi‐scale features on expanded perception fields for local features, and a multi‐level fusing attention module is introduced for efficient context information extraction. Paired t‐test is conducted to evaluate the significant difference in dice between the proposed methods and the comparing methods.ResultsTwo datasets are constructed from the original MSD dataset. For each dataset, 50 cases are randomly selected for model evaluation in the scheme of 5‐fold cross‐validation. The results show that our method outperforms the state‐of‐the‐art Convolutional Neural Network‐based and transformer‐based methods. Specifically, for the first dataset, our model reaches 0.815, 0.830, and 0.807 at overall dice, precision, and sensitivity. The dice of the hepatic and PVs are 0.835 and 0.796, which also exceed the numeric result of the comparing methods. Almost all the p‐values of paired t‐tests on the proposed approach and comparing approaches are smaller than 0.05. On the second dataset, the proposed algorithm achieves 0.749, 0.762, 0.726, 0.835, and 0.796 for overall dice, precision, sensitivity, dice for HV, and dice for PV, among which the first four numeric results exceed comparing methods.ConclusionsThe proposed method is effective in solving the problem of misclassifying interlaced peripheral veins for the HV and PV segmentation task and outperforming the comparing methods on the relabeled dataset.

An open-source, three-dimensional growth model of the mandible

The available reference data for the mandible and mandibular growth consists primarily of two-dimensional linear or angular measurements. The aim of this study was to create the first open-source, three-dimensional statistical shape model of the mandible that spans the complete growth period. Computed tomography scans of 678 mandibles from children and young adults between 0 and 22 years old were included in the model. The mandibles were segmented using a semi-automatic or automatic (artificial intelligence-based) segmentation method. Point correspondence among the samples was achieved by rigid registration, followed by non-rigid registration of a symmetrical template onto each sample. The registration process was validated with adequate results. Principal component analysis was used to gain insight in the variation within the dataset and to investigate age-related changes and sexual dimorphism. The presented growth model is accessible globally and free-of-charge for scientists, physicians and forensic investigators for any kind of purpose deemed suitable. The versatility of the model opens up new possibilities in the fields of oral and maxillofacial surgery, forensic sciences or biological anthropology. In clinical settings, the model may aid diagnostic decision-making, treatment planning and treatment evaluation.

Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis

The carotid artery is a vital blood vessel that supplies oxygenated blood to the brain. Blockages in this artery can lead to life-threatening illnesses, making accurate diagnosis essential. While ultrasound (US) imaging is the primary diagnostic tool, evaluations by trained operators can be subjective and imprecise. An automated approach can provide a more reliable and accurate evaluation of the carotid artery’s condition. To achieve this, the artery must first be identified and isolated from US images. This paper proposes an automated segmentation method for the carotid artery in transverse B-mode ultrasound images, using a Bi-attention DoubleUnet architecture which incorporates spatial attention and channel wise attention using Bottleneck attention module. The method is evaluated on a dataset of transverse images acquired from two devices. The obtained results exhibit higher performance than existing methods with a dice index of 95.96%, IoU of 97.92%, Precision of 98.35% and recall of 97.57% on combined dataset. Another variant of doubleUnet with SE layer and without ASPP (Artous Spatial Pyramidal Pooling) is presented where the modified DoubleUnet with SE realized an average IoU of 92.805%, Dice of 96.215%, precision of 98.82%, and recall of 93.84% representing average results across datasets. These approaches can significantly improve the efficiency and accuracy of carotid artery disease diagnosis and treatment, ultimately improving patient outcomes.

Automatic Segmentation of Bone Marrow Lesions on MRI Using a Deep Learning Method.

Bone marrow lesion (BML) volume is a potential biomarker of knee osteoarthritis (KOA) as it is associated with cartilage degeneration and pain. However, segmenting and quantifying the BML volume is challenging due to the small size, low contrast, and various positions where the BML may occur. It is also time-consuming to delineate BMLs manually. In this paper, we proposed a fully automatic segmentation method for BMLs without requiring human intervention. The model takes intermediate weighted fat-suppressed (IWFS) magnetic resonance (MR) images as input, and the output BML masks are evaluated using both regular 2D Dice similarity coefficient (DSC) of the slice-level area metric and 3D DSC of the subject-level volume metric. On a dataset with 300 subjects, each subject has a sequence of 36 IWFS MR images approximately. We randomly separated the dataset into training, validation, and testing sets with a 70%/15%/15% split at the subject level. Since not every subject or image has a BML, we excluded the images without a BML in each subset. The ground truth of the BML was labeled by trained medical staff using a semi-automatic tool. Compared with the ground truth, the proposed segmentation method achieved a Pearson's correlation coefficient of 0.98 between the manually measured volumes and automatically segmented volumes, a 2D DSC of 0.68, and a 3D DSC of 0.60 on the testing set. Although the DSC result is not high, the high correlation of 0.98 indicates that the automatically measured BML volume is strongly correlated with the manually measured BML volume, which shows the potential to use the proposed method as an automatic measurement tool for the BML biomarker to facilitate the assessment of knee OA progression.

A prior-information-based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer.

Adaptive planning to accommodate anatomic changes during treatment often requires repeated segmentation. In this study, prior patient-specific data was integrateda into a registration-guided multi-channel multi-path (Rg-MCMP) segmentation framework to improve the accuracy of repeated clinical target volume (CTV) segmentation. This study was based on CT image datasets for a total of 90 cervical cancer patients who received two courses of radiotherapy. A total of 15 patients were selected randomly as the test set. In the Rg-MCMP segmentation framework, the first-course CT images (CT1) were registered to second-course CT images (CT2) to yield aligned CT images (aCT1), and the CTV in the first course (CTV1) was propagated to yield aligned CTV contours (aCTV1). Then, aCT1, aCTV1, and CT2 were combined as the inputs for 3D U-Net consisting of a channel-based multi-path feature extraction network. The performance of the Rg-MCMP segmentation framework was evaluated and compared with the single-channel single-path model (SCSP), the standalone registration methods, and the registration-guided multi-channel single-path (Rg-MCSP) model. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) were used as the metrics. The average DSC of CTV for the deformable image DIR-MCMP model was found to be 0.892, greater than that of the standalone DIR (0.856), SCSP (0.837), and DIR-MCSP (0.877), which were improvements of 4.2%, 6.6%, and 1.7%, respectively. Similarly, the rigid body DIR-MCMP model yielded an average DSC of 0.875, which exceeded standalone RB (0.787), SCSP (0.837), and registration-guided multi-channel single-path (0.848), which were improvements of 11.2%, 4.5%, and 3.2%, respectively. These improvements in DSC were statistically significant (p<0.05). The proposed Rg-MCMP framework achieved excellent accuracy in CTV segmentation as part of the adaptive radiotherapy workflow.

Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning

ObjectiveTo develop a deep learning (DL) model for segmenting fat metaplasia (FM) on sacroiliac joint (SIJ) MRI and further develop a DL model for classifying axial spondyloarthritis (axSpA) and non-axSpA.Materials and methodsThis study retrospectively collected 706 patients with FM who underwent SIJ MRI from center 1 (462 axSpA and 186 non-axSpA) and center 2 (37 axSpA and 21 non-axSpA). Patients from center 1 were divided into the training, validation, and internal test sets (n = 455, 64, and 129). Patients from center 2 were used as the external test set. We developed a UNet-based model to segment FM. Based on segmentation results, a classification model was built to distinguish axSpA and non-axSpA. Dice Similarity Coefficients (DSC) and area under the curve (AUC) were used for model evaluation. Radiologists’ performance without and with model assistance was compared to assess the clinical utility of the models.ResultsOur segmentation model achieved satisfactory DSC of 81.86% ± 1.55% and 85.44% ± 6.09% on the internal cross-validation and external test sets. The classification model yielded AUCs of 0.876 (95% CI: 0.811–0.942) and 0.799 (95% CI: 0.696–0.902) on the internal and external test sets, respectively. With model assistance, segmentation performance was improved for the radiological resident (DSC, 75.70% vs. 82.87%, p < 0.05) and expert radiologist (DSC, 85.03% vs. 85.74%, p > 0.05).ConclusionsDL is a novel method for automatic and accurate segmentation of FM on SIJ MRI and can effectively increase radiologist’s performance, which might assist in improving diagnosis and progression of axSpA.Critical relevance statementDL models allowed automatic and accurate segmentation of FM on sacroiliac joint MRI, which might facilitate quantitative analysis of FM and have the potential to improve diagnosis and prognosis of axSpA.Key points• Deep learning was used for automatic segmentation of fat metaplasia on MRI.• UNet-based models achieved automatic and accurate segmentation of fat metaplasia.• Automatic segmentation facilitates quantitative analysis of fat metaplasia to improve diagnosis and prognosis of axial spondyloarthritis.Graphical

Improved MRI Brain Tumor Segmentation Method Based on UNet++

Brain tumors are one of the most common tumors with high mortality. Accurate detection and quantitative evaluation of brain tumors are necessary for patient treatment. A reliable and effective method for automatic segmentation of brain tumors by magnetic resonance imaging (MRI) plays a crucial role in patient treatment. In recent years, the Unet++ network structure has shown better performance in image segmentation. However, it is deficient in small-scale feature extraction and segmentation accuracy. In this study, the AC-ResUnet++ network was proposed to address the problems of Unet++ network mentioned above, which combined residual module, convolutional block attention module (CBAM) and Atrous spatial pyramidal pooling (ASPP). The residual module can solve the problem of gradient explosion in the deep network, CBAM can focus on local feature information and enhance the feature learning ability of the network for the target range, and ASPP module can generate different receptive fields and obtain multi-scale contextual feature information. The BraTS 2018 and BraTS 2019 datasets were used to test the proposed network. The Dice similarity coefficient of AC-ResUnet++ in whole tumor, tumor core and enhanced tumor are 0.8521, 0.8696 and 0.7868, respectively. Compared with Unet++ network, the Dice similarity coefficient increased by 1.22%, 5.08% and 2.03% on the baselines. The comprehensive results demonstrate that AC-ResUnet++ performs more effectively in improving the segmentation accuracy of brain tumor.

AMS-U-Net: automatic mass segmentation in digital breast tomosynthesis via U-Net.

The objective of this study was to develop a fully automatic mass segmentation method called AMS-U-Net for digital breast tomosynthesis (DBT), a popular breast cancer screening imaging modality. The aim was to address the challenges posed by the increasing number of slices in DBT, which leads to higher mass contouring workload and decreased treatment efficiency. The study used 50 slices from different DBT volumes for evaluation. The AMS-U-Net approach consisted of four stages: image pre-processing, AMS-U-Net training, image segmentation, and post-processing. The model performance was evaluated by calculating the true positive ratio (TPR), false positive ratio (FPR), F-score, intersection over union (IoU), and 95% Hausdorff distance (pixels) as they are appropriate for datasets with class imbalance. The model achieved 0.911, 0.003, 0.911, 0.900, 5.82 for TPR, FPR, F-score, IoU, and 95% Hausdorff distance, respectively. The AMS-U-Net model demonstrated impressive visual and quantitative results, achieving high accuracy in mass segmentation without the need for human interaction. This capability has the potential to significantly increase clinical efficiency and workflow in DBT for breast cancer screening.

Choroidal Vascularity Index and Choroidal Structural Changes in Children With Nephrotic Syndrome.

To investigate the choroidal vascularity index (CVI) and choroidal structural changes in children with nephrotic syndrome. This was a cross-sectional study involving 45 children with primary nephrotic syndrome and 40 normal controls. All participants underwent enhanced depth imaging-optical coherence tomography examinations. An automatic segmentation method based on deep learning was used to segment the choroidal vessels and stroma, and the choroidal volume (CV), vascular volume (VV), and CVI within a 4.5mm diameter circular area centered around the macular fovea were obtained. Clinical data, including blood lipids, serum proteins, renal function, and renal injury indicators, were collected from the patients. Compared with normal controls, children with nephrotic syndrome had a significant increase in CV (nephrotic syndrome: 4.132 ± 0.464 vs. normal controls: 3.873 ± 0.574; P = 0.024); no significant change in VV (nephrotic syndrome: 1.276 ± 0.173 vs. normal controls: 1.277 ± 0.165; P = 0.971); and a significant decrease in the CVI (nephrotic syndrome: 0.308 [range, 0.270-0.386] vs. normal controls: 0.330 [range, 0.288-0.387]; P < 0.001). In the correlation analysis, the CVI was positively correlated with serum total protein, serum albumin, serum prealbumin, ratio of serum albumin to globulin, and 24-hour urine volume and was negatively correlated with total cholesterol, low-density lipoprotein cholesterol, urinary protein concentration, and ratio of urinary transferrin to creatinine (all P < 0.05). The CVI is significantly reduced in children with nephrotic syndrome, and the decrease in the CVI parallels the severity of kidney disease, indicating choroidal involvement in the process of nephrotic syndrome. Our findings contribute to a deeper understanding of how nephrotic syndrome affects the choroid.