Ultrasound Image Segmentation Research Articles

PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head

During the process of labor, the intrapartum transperineal ultrasound examination serves as a valuable tool, allowing direct observation of the relative positional relationship between the pubic symphysis and fetal head (PSFH). Accurate assessment of fetal head descent and the prediction of the most suitable mode of delivery heavily rely on this relationship. However, achieving an objective and quantitative interpretation of the ultrasound images necessitates precise PSFH segmentation (PSFHS), a task that is both time-consuming and demanding. Integrating the potential of artificial intelligence (AI) in the field of medical ultrasound image segmentation, the development and evaluation of AI-based models rely significantly on access to comprehensive and meticulously annotated datasets. Unfortunately, publicly accessible datasets tailored for PSFHS are notably scarce. Bridging this critical gap, we introduce a PSFHS dataset comprising 1358 images, meticulously annotated at the pixel level. The annotation process adhered to standardized protocols and involved collaboration among medical experts. Remarkably, this dataset stands as the most expansive and comprehensive resource for PSFHS to date.

A temporal enhanced semi-supervised training framework for needle segmentation in 3D ultrasound images.

Automated biopsy needle segmentation in 3D ultrasound images can be used for biopsy navigation, but it is quite challenging due to the low ultrasound image resolution and interference similar to the needle appearance. For 3D medical image segmentation, such deep learning (DL) networks as convolutional neural network (CNN) and transformer have been investigated. However, these segmentation methods require numerous labeled data for training, have difficulty in meeting the real-time segmentation requirement and involve high memory consumption.
Approach. In this paper, we have proposed the temporal information-based semi-supervised training framework for fast and accurate needle segmentation. Firstly, a novel circle transformer module based on the static and dynamic features has been designed after the encoders for extracting and fusing the temporal information. Then, the consistency constraints of the outputs before and after combining temporal information are proposed to provide the semi-supervision for the unlabeled volume. Finally, the model is trained using the loss function which combines the cross-entropy and Dice similarity coefficient (DSC) based segmentation loss with mean square error based consistency loss. The trained model with the single ultrasound volume input is applied to realize the needle segmentation in ultrasound volume.
Main results. Experimental results on three needle ultrasound datasets acquired during the beagle biopsy show that our approach is superior to the most competitive mainstream temporal segmentation model and semi-supervised method by providing higher DSC (77.1% vs 76.5%), smaller needle tip position (1.28mm vs 1.87mm) and length (1.78mm vs 2.19mm) errors on the kidney dataset as well as DSC (78.5% vs 76.9%), needle tip position (0.86mm vs 1.12mm) and length (1.01mm vs 1.26mm) errors on the prostate dataset.
Significance. The proposed method can significantly enhance needle segmentation accuracy by training with sequential images at no additional cost. This enhancement may further improve the effectiveness of biopsy navigation systems.&#xD.

EH-former: Regional easy-hard-aware transformer for breast lesion segmentation in ultrasound images

Breast lesion segmentation of ultrasound images plays a crucial role in early screening and diagnosis of breast lesions. However, accurately segmenting lesions in breast ultrasound (BUS) images is challenging due to prevalent issues such as low contrast, intense speckle noise, and blurred lesion boundaries. Although existing deep learning-based segmentation models have made significant progress, few have strategically addressed these complex and noisy regional features in BUS images. The easy-to-hard manner in Curriculum Learning (CL) appears promising, but it often remains at the sample level and does not adequately address regional complexities. To address this, we design a region-wise CL to dynamically adjust the focus on hard regional features in BUS images. Specifically, we propose a Regional Easy-Hard-Aware Transformer (EH-Former), structured in two stages for lesion segmentation in BUS images. The first stage incorporates uncertainty estimation for dividing regional difficulty. In the second stage, we propose a novel Adaptive Easy-Hard region Separator (AdaSep), a module employing uncertainty-aware regularization to separate features of varying difficulties, allowing the two streams within EH-Former to focus on learning regional features of different complexities. Additionally, we develop a Dynamic Easy-Hard Feature Fusion (D-Fusion) module, dynamically adjusting the fusion weight of easy and hard regional features based on the current training epoch to achieve progressive regional feature learning. Extensive experimental results on five public datasets show that the proposed EH-Former consistently outperforms state-of-the-art methods in most metrics and exhibits better domain generalization capabilities. Furthermore, our region-wise CL significantly enhances the performance of EH-Former in detecting complex tissue structures and noisy areas that are challenging to segment accurately. The source code is available at https://github.com/lele0109/EH-Former.

Automated Classification of Abdominal Ultrasound lmages of the Pancreas Based on the Spectral Representation of the Border’s Contours

The purpose of the research is to develop a methodology for classifying complexly structured halftone images based on a multimodal approach using methods of morphological analysis, spectral analysis and neural network modeling.Methods. А method for classifying the contours of the boundaries of segments of a complexly structured image is described. Тhe method is based on the fact that in chronic diseases of the pancreas, there is a violation of the integrity of the contour of its border and its waviness increases due to retractions and bulges caused by an alterative inflammatory process. Тhe method includes the stages of normalization of ultrasound images and image segmentation with the selection of the contour of the object of interest. Тo classify the contour of a segment boundary, it is proposed to use Fourier analysis and neural network technologies. Тhe method is illustrated using the example of classifying the contour of the border of the pancreas on its transcutaneous acoustic image.Results. Еxperimental studies of the proposed methods and means for classifying medical risk were carried out on diagnostic tasks according to the following classes: "chronic pancreatitis" – "without pathology". For experimental studies, video sequences of ultrasound images of the pancreas provided by an endoscopist were used. Тhe purpose of the experimental studies was to analyze the classification quality indicators of image classifiers with class segments "Chronic pancreatitis" and "Without pathology". Тhe training sample of video images (frames of video sequences) included 200 examples, one hundred from each class. Тhe quality indicator "Sensitivity" of classification for two classes is 85,7%, the indicator "Specificity" is 87,1%.Тhe use of the contour analysis method in classifiers of ultrasound images of the pancreas opens up new opportunities for accessible and objective diagnosis of pancreatic diseases, expanding the capabilities of intelligent clinical decision support systems.

Deep Learning Detection and Segmentation of Facet Joints in Ultrasound Images Based on Convolutional Neural Networks and Enhanced Data Annotation.

The facet joint injection is the most common procedure used to release lower back pain. In this paper, we proposed a deep learning method for detecting and segmenting facet joints in ultrasound images based on convolutional neural networks (CNNs) and enhanced data annotation. In the enhanced data annotation, a facet joint was considered as the first target and the ventral complex as the second target to improve the capability of CNNs in recognizing the facet joint. A total of 300 cases of patients undergoing pain treatment were included. The ultrasound images were captured and labeled by two professional anesthesiologists, and then augmented to train a deep learning model based on the Mask Region-based CNN (Mask R-CNN). The performance of the deep learning model was evaluated using the average precision (AP) on the testing sets. The data augmentation and data annotation methods were found to improve the AP. The AP50 for facet joint detection and segmentation was 90.4% and 85.0%, respectively, demonstrating the satisfying performance of the deep learning model. We presented a deep learning method for facet joint detection and segmentation in ultrasound images based on enhanced data annotation and the Mask R-CNN. The feasibility and potential of deep learning techniques in facet joint ultrasound image analysis have been demonstrated.

Distraction-aware hierarchical learning for vascular structure segmentation in intravascular ultrasound images

Vascular structure segmentation in intravascular ultrasound (IVUS) images plays an important role in pre-procedural evaluation of percutaneous coronary intervention (PCI). However, vascular structure segmentation in IVUS images has the challenge of structure-dependent distractions. Structure-dependent distractions are categorized into two cases, structural intrinsic distractions and inter-structural distractions. Traditional machine learning methods often rely solely on low-level features, overlooking high-level features. This way limits the generalization of these methods. The existing semantic segmentation methods integrate low-level and high-level features to enhance generalization performance. But these methods also introduce additional interference, which is harmful to solving structural intrinsic distractions. Distraction cue methods attempt to address structural intrinsic distractions by removing interference from the features through a unique decoder. However, they tend to overlook the problem of inter-structural distractions. In this paper, we propose distraction-aware hierarchical learning (DHL) for vascular structure segmentation in IVUS images. Inspired by distraction cue methods for removing interference in a decoder, the DHL is designed as a hierarchical decoder that gradually removes structure-dependent distractions. The DHL includes global perception process, distraction perception process and structural perception process. The global perception process and distraction perception process remove structural intrinsic distractions then the structural perception process removes inter-structural distractions. In the global perception process, the DHL searches for the coarse structural region of the vascular structures on the slice of IVUS sequence. In the distraction perception process, the DHL progressively refines the coarse structural region of the vascular structures to remove structural distractions. In the structural perception process, the DHL detects regions of inter-structural distractions in fused structure features then separates them. Extensive experiments on 361 subjects show that the DHL is effective (e.g., the average Dice is greater than 0.95), and superior to ten state-of-the-art IVUS vascular structure segmentation methods.

Automatic breast mass segmentation in ultrasound images with U-Net and resolution enhancement blocks

This research proposes a deep learning method for breast mass segmentation in 2D ultrasound images. The low quality of ultrasound images and indistinct boundaries of masses cause difficulties in the automatic segmentation of breast masses. To solve this problem, we introduce an autoencoder-based model that is equipped with resolution enhancement blocks. The proposed model is composed of U-Net, along with residual blocks and attention blocks. Its objective is to increase the accuracy of breast mass segmentation by enhancing the feature maps and paying more attention to the region of interest, and preventing the loss of features in the encoder part of U-Net, this helps to rebuild an image with better quality in the decoder section. The proposed model achieved an accuracy of 98.84% and a Dice score of 84.72% for image containing benign tumor and 82.14% for image containing malignant tumor. For the IoU metric, the model obtained scores of 85.95% and 82.21% for images containing benign and malignant tumors, respectively. These results showed an improvement in segmentation accuracy compared to the performance of previous models based on U-Net. This highlights the usefulness and advantage of using U-Net and resolution enhancement blocks for breast mass segmentation11The code for the implemented model can be found in the article's GitHub..

MF-Net: Multiple-feature extraction network for breast lesion segmentation in ultrasound images

Objective:Breast lesion segmentation in ultrasound images is of great significance for qualitative breast lesions. However, blurred lesion boundaries, irregular lesion shapes, and similar intensity distributions between lesion and background bring challenges to accurately segmenting breast lesions. Recently, several U-Net-based variants and transformer-based networks have been applied in breast lesion segmentation. Nevertheless, these methods have three limitations: (1) Introducing mass attention mechanisms and complex operations, (2) Ignoring the capability of extracting local detail features, and (3) Ignoring fusing different global semantic information. Approach:To alleviate these challenges, we propose a novel multiple-feature extraction network named MF-Net. The core designs are as follows: (1) A transformer-based auxiliary bi-encoder models long-range dependencies, (2) A multiple-feature extraction module excavates the robust local features, and (3) A global feature enhancement module integrates different global context information. Main results:We conducted extensive experiments on three public breast lesion datasets and compared our method with twelve state-of-the-art methods. The results of comparison experiments indicate that our model achieves a 3.16% improvement in boundary accuracy with a slight lead in segmentation accuracy. Similarly, when the boundary accuracy is close, our model also gets a 3.92% improvement in segmentation accuracy. In the generalization experiments, our network also demonstrates varying degrees of superiority in segmentation accuracy and boundary precision. Significance:Our network mainly has three advantages: (1) It achieves outstanding performance in the precise segmentation of the breast lesions, (2) It focuses on exploring details such as irregular morphology and blurred boundaries of breast lesions, providing valuable insights for further clinical research, and (3) It improves performance without decreasing inference speed through the introduction of critical components.

When deep learning is not enough: artificial life as a supplementary tool for segmentation of ultrasound images of breast cancer.

Segmentation of tumors in ultrasound (US) images of the breast is a critical issue in medical imaging. Due to the poor quality of US images and the varying specifications of US machines, segmentation and classification of abnormalities present difficulties even for trained radiologists. The paper aims to introduce a novel AI-based hybrid model for US segmentation that offers high accuracy, requires relatively smaller datasets, and is capable of handling previously unseen data. The software can be used for diagnostics and the US-guided biopsies. A unique and robust hybrid approach that combines deep learning (DL) and multi-agent artificial life (AL) has been introduced. The algorithms are verified on three US datasets. The method outperforms 14 selected state-of-the-art algorithms applied to US images characterized by complex geometry and high level of noise. The paper offers an original classification of the images and tests to analyze the limits of the DL. The model has been trained and verified on 1264 ultrasound images. The images are in the JPEG and PNG formats. The age of the patients ranges from 22 to 73 years. The 14 benchmark algorithms include deformable shapes, edge linking, superpixels, machine learning, and DL methods. The tests use eight-region shape- and contour-based evaluation metrics. The proposed method (DL-AL) produces excellent results in terms of the dice coefficient (region) and the relative Hausdorff distance H3 (contour-based) as follows: the easiest image complexity level, Dice = 0.96 and H3 = 0.26; the medium complexity level, Dice = 0.91 and H3 = 0.82; and the hardest complexity level, Dice = 0.90 and H3 = 0.84. All other metrics follow the same pattern. The DL-AL outperforms the second best (Unet-based) method by 10-20%. The method has been also tested by a series of unconventional tests. The model was trained on low complexity images and applied to the entire set of images. These results are summarized below. (1) Only the low complexity images have been used for training (68% unknown images): Dice = 0.80 and H3 = 2.01. (2) The low and the medium complexity images have been used for training (51% unknown images): Dice = 0.86 and H3 = 1.32. (3) The low, medium, and hard complexity images have been used for training (35% unknown images): Dice = 0.92 and H3 = 0.76. These tests show a significant advantage of DL-AL over 30%. A video demo illustrating the algorithm is at http://tinyurl.com/mr4ah687 .

DBL-Net: A dual-branch learning network with information from spatial and frequency domains for tumor segmentation and classification in breast ultrasound image

Breast cancer is a common malignancy, ranking as one of the most prevalent cancers among women globally. Improving treatment success rates for breast cancer are crucially dependent on early detection and accurate diagnosis. Ultrasound imaging, as a non-invasive and low-radiation imaging technique, plays a significant role in early breast cancer detection. However, due to the characteristics of fuzzy tumor boundaries and irregular shapes of the tumor, accurate breast tumor classification and segmentation remain challenging tasks. This paper introduces a novel approach for breast ultrasound image segmentation and classification tasks. Firstly, we study the potential value of frequency domain information in breast image analysis and propose the DBL-Net, a multi-task learning network for simultaneous tumor segmentation and tumor classification. Secondly, we customize a Spatial-Frequency Domain Feature Encoding module (S-FEM) to better encode and fuse spatial and frequency domain features of breast ultrasound images. Furthermore, we design a Representational Perception Enhancer (RPE) to improve the feature representation capabilities and prediction accuracy of the model. Finally, We have carried out a comprehensive series of tests on the BUS dataset, and the findings clearly indicate that our method outperforms the current state-of-the-art methods.

MEF-UNet: An end-to-end ultrasound image segmentation algorithm based on multi-scale feature extraction and fusion

Ultrasound image segmentation is a challenging task due to the complexity of lesion types, fuzzy boundaries, and low-contrast images along with the presence of noises and artifacts. To address these issues, we propose an end-to-end multi-scale feature extraction and fusion network (MEF-UNet) for the automatic segmentation of ultrasound images. Specifically, we first design a selective feature extraction encoder, including detail extraction stage and structure extraction stage, to precisely capture the edge details and overall shape features of the lesions. In order to enhance the representation capacity of contextual information, we develop a context information storage module in the skip-connection section, responsible for integrating information from adjacent two-layer feature maps. In addition, we design a multi-scale feature fusion module in the decoder section to merge feature maps with different scales. Experimental results indicate that our MEF-UNet can significantly improve the segmentation results in both quantitative analysis and visual effects.

US2Mask: Image-to-mask generation learning via a conditional GAN for cardiac ultrasound image segmentation

Cardiac ultrasound (US) image segmentation is vital for evaluating clinical indices, but it often demands a large dataset and expert annotations, resulting in high costs for deep learning algorithms. To address this, our study presents a framework utilizing artificial intelligence generation technology to produce multi-class RGB masks for cardiac US image segmentation. The proposed approach directly performs semantic segmentation of the heart’s main structures in US images from various scanning modes. Additionally, we introduce a novel learning approach based on conditional generative adversarial networks (CGAN) for cardiac US image segmentation, incorporating a conditional input and paired RGB masks. Experimental results from three cardiac US image datasets with diverse scan modes demonstrate that our approach outperforms several state-of-the-art models, showcasing improvements in five commonly used segmentation metrics, with lower noise sensitivity. Source code is available at https://github.com/energy588/US2mask.

Ultrasound image segmentation of renal tumors based on UNet++ with fusion of multiscale residuals and dual attention

Objective. Laparoscopic renal unit-preserving resection is a routine and effective means of treating renal tumors. Image segmentation is an essential part before tumor resection. The current segmentation method mainly relies on doctors manual delineation, which is time-consuming, labor-intensive, and influenced by their personal experience and ability. And the image quality of segmentation is low, with problems such as blurred edges, unclear size and shape, which are not conducive to clinical diagnosis. Approach. To address these problems, we propose an automated segmentation method, i.e. the UNet++ algorithm fusing multiscale residuals and dual attention (MRDA_UNet++). It replaces two consecutive 3 × 3 convolutions in UNet++ with the ‘MultiRes block’ module, which incorporates coordinate attention to fuse features from different scales and suppress the impact of background noise. Furthermore, an attention gate is also added at the short connections to enhance the ability of the network to extract features from the target area. Main results. The experimental results show that MRDA_UNet++ achieves 93.18%, 92.87%, 93.66%, and 92.09% on the real-world dataset for MIoU, Dice, Precision, and Recall, respectively. Compared to the baseline model UNet++ on three public datasets, the MIoU, Dice, and Recall metrics improved by 6.00%, 7.90% and 18.09% respectively for BUSI, 0.39%, 0.27% and 1.03% for Dataset C, and 1.37%, 1.75% and 1.30% for DDTI. Significance. The proposed MRDA_UNet++ exhibits obvious advantages in feature extraction, which can not only significantly reduce the workload of doctors, but also further decrease the risk of misdiagnosis. It is of great value to assist doctors diagnosis in the clinic.

Three-dimensional visualization of thyroid ultrasound images based on multi-scale features fusion and hierarchical attention.

Ultrasound three-dimensional visualization, a cutting-edge technology in medical imaging, enhances diagnostic accuracy by providing a more comprehensive and readable portrayal of anatomical structures compared to traditional two-dimensional ultrasound. Crucial to this visualization is the segmentation of multiple targets. However, challenges like noise interference, inaccurate boundaries, and difficulties in segmenting small structures exist in the multi-target segmentation of ultrasound images. This study, using neck ultrasound images, concentrates on researching multi-target segmentation methods for the thyroid and surrounding tissues. We improved the Unet++ to propose PA-Unet++ to enhance the multi-target segmentation accuracy of the thyroid and its surrounding tissues by addressing ultrasound noise interference. This involves integrating multi-scale feature information using a pyramid pooling module to facilitate segmentation of structures of various sizes. Additionally, an attention gate mechanism is applied to each decoding layer to progressively highlight target tissues and suppress the impact of background pixels. Video data obtained from 2D ultrasound thyroid serial scans served as the dataset for this paper.4600 images containing 23,000 annotated regions were divided into training and test sets at a ratio of 9:1, the results showed that: compared with the results of U-net++, the Dice of our model increased from 78.78% to 81.88% (+ 3.10%), the mIOU increased from 73.44% to 80.35% (+ 6.91%), and the PA index increased from 92.95% to 94.79% (+ 1.84%). Accurate segmentation is fundamental for various clinical applications, including disease diagnosis, treatment planning, and monitoring. This study will have a positive impact on the improvement of 3D visualization capabilities and clinical decision-making and research in the context of ultrasound image.

MSCA-UNet: multi-scale channel attention-based UNet for segmentation of medical ultrasound images

MSCA-UNet: multi-scale channel attention-based UNet for segmentation of medical ultrasound images

Artificial intelligence model for segmentation and severity scoring of osteophytes in hand osteoarthritis on ultrasound images.

To develop an artificial intelligence (AI) model able to perform both segmentation of hand joint ultrasound images for osteophytes, bone, and synovium and perform osteophyte severity scoring following the EULAR-OMERACT grading system (EOGS) for hand osteoarthritis (OA). One hundred sixty patients with pain or reduced function of the hands were included. Ultrasound images of the metacarpophalangeal (MCP), proximal interphalangeal (PIP), distal interphalangeal (DIP), and first carpometacarpal (CMC1) joints were then manually segmented for bone, synovium and osteophytes and scored from 0 to 3 according to the EOGS for OA. Data was divided into a training, validation, and test set. The AI model was trained on the training data to perform bone, synovium, and osteophyte identification on the images. Based on the manually performed image segmentation, an AI was trained to classify the severity of osteophytes according to EOGS from 0 to 3. Percent Exact Agreement (PEA) and Percent Close Agreement (PCA) were assessed on individual joints and overall. PCA allows a difference of one EOGS grade between doctor assessment and AI. A total of 4615 ultrasound images were used for AI development and testing. The developed AI model scored on the test set for the MCP joints a PEA of 76% and PCA of 97%; for PIP, a PEA of 70% and PCA of 97%; for DIP, a PEA of 59% and PCA of 94%, and CMC a PEA of 50% and PCA of 82%. Combining all joints, we found a PEA between AI and doctor assessments of 68% and a PCA of 95%. The developed AI model can perform joint ultrasound image segmentation and severity scoring of osteophytes, according to the EOGS. As proof of concept, this first version of the AI model is successful, as the agreement performance is slightly higher than previously found agreements between experts when assessing osteophytes on hand OA ultrasound images. The segmentation of the image makes the AI explainable to the doctor, who can immediately see why the AI applies a given score. Future validation in hand OA cohorts is necessary though.

TaiChiNet: Negative-Positive Cross-Attention Network for Breast Lesion Segmentation in Ultrasound Images.

Breast lesion segmentation in ultrasound images is essential for computer-aided breast-cancer diagnosis. To improve the segmentation performance, most approaches design sophisticated deep-learning models by mining the patterns of foreground lesions and normal backgrounds simultaneously or by unilaterally enhancing foreground lesions via various focal losses. However, the potential of normal backgrounds is underutilized, which could reduce false positives by compacting the feature representation of all normal backgrounds. From a novel viewpoint of bilateral enhancement, we propose a negative-positive cross-attention network to concentrate on normal backgrounds and foreground lesions, respectively. Derived from the complementing opposites of bipolarity in TaiChi, the network is denoted as TaiChiNet, which consists of the negative normal-background and positive foreground-lesion paths. To transmit the information across the two paths, a cross-attention module, a complementary MLP-head, and a complementary loss are built for deep-layer features, shallow-layer features, and mutual-learning supervision, separately. To the best of our knowledge, this is the first work to formulate breast lesion segmentation as a mutual supervision task from the foreground-lesion and normal-background views. Experimental results have demonstrated the effectiveness of TaiChiNet on two breast lesion segmentation datasets with a lightweight architecture. Furthermore, extensive experiments on the thyroid nodule segmentation and retinal optic cup/disc segmentation datasets indicate the application potential of TaiChiNet.

SC-Unext: A Lightweight Image Segmentation Model with Cellular Mechanism for Breast Ultrasound Tumor Diagnosis.

Automatic breast ultrasound image segmentation plays an important role in medical image processing. However, current methods for breast ultrasound segmentation suffer from high computational complexity and large model parameters, particularly when dealing with complex images. In this paper, we take the Unext network as a basis and utilize its encoder-decoder features. And taking inspiration from the mechanisms of cellular apoptosis and division, we design apoptosis and division algorithms to improve model performance. We propose a novel segmentation model which integrates the division and apoptosis algorithms and introduces spatial and channel convolution blocks into the model. Our proposed model not only improves the segmentation performance of breast ultrasound tumors, but also reduces the model parameters and computational resource consumption time. The model was evaluated on the breast ultrasound image dataset and our collected dataset. The experiments show that the SC-Unext model achieved Dice scores of 75.29% and accuracy of 97.09% on the BUSI dataset, and on the collected dataset, it reached Dice scores of 90.62% and accuracy of 98.37%. Meanwhile, we conducted a comparison of the model's inference speed on CPUs to verify its efficiency in resource-constrained environments. The results indicated that the SC-Unext model achieved an inference speed of 92.72ms per instance on devices equipped only with CPUs. The model's number of parameters and computational resource consumption are 1.46M and 2.13 GFlops, respectively, which are lower compared to other network models. Due to its lightweight nature, the model holds significant value for various practical applications in the medical field.

Abdominal multi-organ segmentation using multi-scale and context-aware neural networks

Recent advancements in AI have significantly enhanced smart diagnostic methods, bringing us closer to achieving end-to-end diagnosis. Ultrasound image segmentation plays a crucial role in this diagnostic process. An accurate and robust segmentation model accelerates the process and reduces the burden of sonographers. In contrast to previous research, we consider two inherent features of ultrasound images: (1) different organs and tissues vary in spatial sizes, and (2) the anatomical structures inside the human body form a relatively constant spatial relationship. Based on those two ideas, we proposed two segmentation models combining multi-scale convolution neural network backbones and a spatial context feature extractor. We discuss two backbone structures to extract anatomical structures of different scales: the Feature Pyramid Network (FPN) backbone and the Trident Network backbone. Moreover, we show how Spatial Recurrent Neural Network (SRNN) is implemented to extract the spatial context features in abdominal ultrasound images. Our proposed model has achieved dice coefficient score of 0.919 and 0.931, respectively.

GLFNet: Global-local fusion network for the segmentation in ultrasound images

Ultrasound imaging, as a portable and radiation-free modality, presents challenges for accurate segmentation due to the variability of lesions and the similar intensity values of surrounding tissues. Current deep learning approaches leverage convolution for extracting local features and self-attention for handling global dependencies. However, traditional CNNs are spatially local, and Vision Transformers lack image specific bias and are computationally demanding. In response, we propose the Global-Local Fusion Network (GLFNet), a hybrid structure addressing the limitations of both CNNs and Vision Transformers. The GLFNet, featuring Global-Local Fusion Blocks (GLFBlocks), integrates global semantic information with local details to improve segmentation. Each GLFBlock comprises Global and Local Branches for feature extraction in parallel. Within the Global and Local Branches, we introduce the Self-Attention Convolution Fusion Block (SACFBlock), which includes a Spatial-Attention Module and Channel-Attention Module. Experimental results show that our proposed GLFNet surpasses its counterparts in the segmentation tasks, achieving the overall best results with an mIoU of 79.58% and Dice coefficient of 74.62% in the DDTI dataset, an mIoU of 76.61% and Dice coefficient of 71.04% in the BUSI dataset, and an mIoU of 86.77% and Dice coefficient of 87.38% in the BUID dataset. The fusion of local and global features contributes to enhanced performance, making GLFNet a promising approach for ultrasound image segmentation.