TSINet: Efficient Breast Lesion Segmentation with Three-Stream Interactive Neural Network on Magnetic Resonance Images

Segmentation of medical ultrasound images is one of the most important functional components of medical ultrasonic instruments for computed aided diagnosis, such as breast lesion early detection and measurement. However, the segmentation of breast lesions from ultrasound images is still a challenging task due to the variance in shape of the lesions and interference from speckle noise. In this paper, a novel approach using tight frames of grouping bandlet(TFGB) and improved stochastic neighbor embedding algorithm(ISNE) is adopted for the segmentation of breast lesions. Novelty in this paper includes that grouplet transform is successively applied and adapted for image segmentation for the first time whereas all of previous works were focused on image inpainting, texture synthesis and image denoising. Moreover, an effective stopping criterion is proposed to improve the dimension reduction technique-stochastic neighbor embedding (SNE) in this paper. Applications to clinical ultrasound images with fibroadenoma and fibrocystic breast lesions contaminated by speckle noise are performed, respectively. Experimental results show that compared with the state-of-the-art approach to the segmentation of breast lesions in ultrasound images, the proposed approach demonstrates robustness to speckle noise and superior performance on the effectiveness.

A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images 1

Ultrasound video-based breast lesion segmentation provides valuable assistance in early breast lesion detection and discrimination. However, this field faces two key challenges: the first is how to simultaneously utilize both intra-frame and inter-frame lesion cues to accurately segment breast lesions, and the second is that the availability of breast ultrasound video datasets is quite limited. In this paper, we propose a novel Spatial-Temporal Progressive Fusion Network (STPFNet) for video-based breast lesion segmentation problem. The proposed STPFNet comprises three main components. First, we propose to adopt a unified network architecture to capture spatial dependencies within each ultrasound frame and temporal correlations between different frames together for feature representation of ultrasound video. Second, we propose a new fusion module called Multi-Granularity Feature Fusion (MGFF) to fuse the extracted information with different granularities for lesion segmentation. MGFF can help improve the issue of lesion boundary blurring. Third, we propose to take the segmentation result of the previous frame as prior knowledge to suppress the noisy background and learn a more robust representation. To further promote the research in this field, we construct a new ultrasound video breast lesion segmentation dataset, called UVBLS200, comprising 200 videos (80 benign and 120 malignant lesions). Experiments on the proposed dataset demonstrate that the proposed STPFNet achieves a better breast lesion detection performance than state-of-the-art methods.

Breast lesion segmentation in ultrasound images is of great importance since it can help us to characterize and localize lesion regions. However, low-quality imaging, blurred boundary, and variable lesion shapes bring challenges to accurate segmentation. In recent years, many U-Net variants have been proposed and successfully applied to breast lesion segmentation. However, these methods suffer from two limitations: (1) Ignoring the ability to capture rich global context information, and (2) Introducing extra complex operations. To alleviate these challenges, we propose a multiscale nonlocal feature extraction network (MNFE-Net) for accurately segmenting breast lesions. The core idea includes three points: (1) Parallel Encoder models long-range dependencies, (2) Multiscale Feature Module refines local features without introducing extra complex operations, and (3) Global Feature Guidance Module extracts global semantic information. MNFE-Net mainly has the following advantages: (1) The method has excellent performance for segmentation of malignant breast lesions, (2) The Parallel Encoder increases network parameters without significantly decreasing inference speed, and (3) The method is easy to understand and execute. Extensive experiment results with six state-of-the-art (SOTA) methods on three public breast ultrasound datasets demonstrate the superior segmentation performance of our proposed MNFE-Net.

Various deep learning methods have been proposed to segment breast lesions from ultrasound images. However, similar intensity distributions, variable tumor morphologies and blurred boundaries present challenges for breast lesions segmentation, especially for malignant tumors with irregular shapes. Considering the complexity of ultrasound images, we develop an adaptive attention U-net (AAU-net) to segment breast lesions automatically and stably from ultrasound images. Specifically, we introduce a hybrid adaptive attention module (HAAM), which mainly consists of a channel self-attention block and a spatial self-attention block, to replace the traditional convolution operation. Compared with the conventional convolution operation, the design of the hybrid adaptive attention module can help us capture more features under different receptive fields. Different from existing attention mechanisms, the HAAM module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation. Extensive experiments with several state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets show that our method has better performance on breast lesions segmentation. Furthermore, robustness analysis and external experiments demonstrate that our proposed AAU-net has better generalization performance in the breast lesion segmentation. Moreover, the HAAM module can be flexibly applied to existing network frameworks. The source code is available on https://github.com/CGPxy/AAU-net.

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

Breast lesion segmentation in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a challenging task since DCE-MRI includes not only spatial information within slices but also temporal correlation between different sequences. Previous works only focused on one-side of the spatio-temporal information, therefore could not obtain accurate segmentation results. In this paper, we propose an automatic breast lesion segmentation framework with much less parameters utilizing both spatial and temporal features. Specifically, a breast region extraction is applied to eliminate unconcerned tissues. After that, the segmentation result can be obtained by a spatio-temporal segmentation network, where we exploit a multi-pathway structure and a fusion operation to unearth more spatio-temporal features. The proposed framework is validated on our real dataset and obtains a more accurate segmentation result compared to other approaches.

Breast lesion segmentation in magnetic resonance (MR) images is one of the most important parts of clinical diagnostic tools. Pixel classification methods have been frequently used in image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to be obtained. On the other hand, unsupervised segmentation methods need no prior knowledge and lead to low performance. However, semi-supervised learning which uses not only a few labeled data, but also a large amount of unlabeled data promises higher accuracy with less effort. In this paper, we propose a new interactive semi-supervised approach to segmentation of suspicious lesions in breast MRI. Using a suitable classifier in this approach has an important role in its performance; in this paper, we present a semi-supervised algorithm improved self-training (IMPST) which is an improved version of self-training method and increase segmentation accuracy. Experimental results show that performance of segmentation in this approach is higher than supervised and unsupervised methods such as K nearest neighbors, Bayesian, Support Vector Machine, and Fuzzy c-Means.

A contrast enhancement method for improving the segmentation of breast lesions on ultrasonography

Breast lesion segmentation is an essential step in developing Computer-aided Diagnostic (CAD) system for the early detection of breast cancer. However, accurate lesion segmentation is challenging, since the lesions may have complicated topological structures and different intensity distributions. This paper describes a novel approach for breast lesion segmentation from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a level set-based active contour model. Its evolution is controlled by specially designed signed pressure function that only accounts for distribution of image background intensity, so solving the weak-edge-passed problem and improving the robustness to segment different lesions. To confirm the proposed approach, it is compared with different methods based on level set. Experiment results show that the proposed method leads to better results. On a total of 38 breast DCE-MRI studies, our method yields a mean difference (MAD) of 7.2561±12.4537 pixels and Jaccard index of 0.8564±0.0493 compared to manual ground truth segmentation.

<p>Segmentation in ultrasound images is challenging due to the interference from speckle noise and fuzziness of boundaries. In this paper, a segmentation scheme using fuzzy c-means (FCM) clustering incorporating both intensity and texture information of images is proposed to extract breast lesions in ultrasound images. Firstly, the nonlinear structure tensor, which can facilitate to refine the edges detected by intensity, is used to extract speckle texture. And then, a spatial FCM clustering is applied on the image feature space for segmentation. In the experiments with simulated and clinical ultrasound images, the spatial FCM clustering with both intensity and texture information gets more accurate results than the conventional FCM or spatial FCM without texture information.</p>

Automated Segmentation of Ultrasonic Breast Lesions Using Statistical Texture Classification and Active Contour Based on Probability Distance

Accurate lesion segmentation is critical for cancer diagnosis and treatment. Convolutional neural networks (CNNs) are widely used for medical image segmentation but struggle to capture long-range dependencies. Transformers mitigate this limitation but come with high computational costs. Mamba, a state-space model (SSM), efficiently models long-range dependencies but lacks precision in fine details. To address these challenges, this study aimed to develop a novel segmentation approach that combines the strengths of CNNs, Transformers, and Mamba, enhancing both global context understanding and local feature extraction in medical image segmentation. We propose AttmNet, a U-shaped network designed for medical image segmentation, which incorporates a novel structure called MAM (Multiscale-Convolution, Self-Attention, and Mamba). The MAM block integrates multi-layer convolution for multi-scale feature learning with an Att-Mamba component that combines self-attention and Mamba to effectively capture global context while preserving fine details. We evaluated AttmNet on four public datasets for breast, skin, and lung lesion segmentation. AttmNet outperformed state-of-the-art methods in terms of intersection over union (IoU) and Dice similarity coefficients. On the breast ultrasound (BUS) dataset, AttmNet achieved a 3.38% improvement in IoU and a 4.54% increase in Dice over the next best method. On the breast ultrasound images (BUSI) dataset, AttmNet's IoU and Dice coefficients were 1.17% and 3.21% higher than the closest competitor, respectively. In the PH2 Dermoscopy Image dataset, AttmNet surpassed the next best model by 0.25% in both IoU and Dice. On the larger coronavirus disease 2019 (COVID-19) Lung dataset, AttmNet maintained strong performance, achieving higher IoU and Dice scores than the next best models, SegMamba and TransUNet. AttmNet is a powerful and efficient tool for medical image segmentation, addressing the limitations of existing methods through its advanced design. The MAM block significantly enhances segmentation accuracy while maintaining computational efficiency, making AttmNet highly suitable for clinical applications. The code is available at https://github.com/hyb2840/AttmNet.

Breast lesions segmentation is an important step of computer-aided diagnosis system, and it has attracted much attention. However, accurate segmentation of malignant breast lesions is a challenging task due to the effects of heterogeneous structure and similar intensity distributions. In this paper, a novel bidirectional aware guidance network (BAGNet) is proposed to segment the malignant lesion from breast ultrasound images. Specifically, the bidirectional aware guidance network is used to capture the context between global (low-level) and local (high-level) features from the input coarse saliency map. The introduction of the global feature map can reduce the interference of surrounding tissue (background) on the lesion regions. To evaluate the segmentation performance of the network, we compared with several state-of-the-art medical image segmentation methods on the public breast ultrasound dataset using six commonly used evaluation metrics. Extensive experimental results indicate that our method achieves the most competitive segmentation results on malignant breast ultrasound images.

Pyramid boundary attention network for breast lesion segmentation in ultrasound images