Medical 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.

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

Research progress on medical ultrasound image segmentation algorithms

Medical ultrasound imaging is an integral part of preoperative diagnosis, lesion screening and ultrasound-guided interventional surgeries. Image segmentation techniques can enhance the identification of lesions and separate them from complex backgrounds, aiding physicians in both quantitative and qualitative analyses. Ultrasound image segmentation algorithms are primarily categorized into two types: traditional non-semantic segmentation and deep learning-based semantic segmentation, each with distinct advantages and drawbacks. This paper delves into these segmentation principles, elucidating their relevance in the realm of ultrasound image segmentation, and offers an overview of current research trends. Our goal is to provide guidance for physicians and researchers in selecting the most suitable segmentation algorithm that tailors to their specific requirements.

Multimodal image feature fusion for improving medical ultrasound image segmentation

Ultrasound imaging is the most commonly used technique to detect abnormalities of the thyroid and diagnose abnormal breasts. However, due to the inherent characteristics of ultrasound imaging, the contrast of ultrasound images is relatively low, and the tissue boundaries in the image are blurry. This makes it a challenging task to segment thyroid or breast lesions from ultrasound images. In this work, we explore the role of multimodal features at different levels of deep neural network models and their fusion mechanisms for multimodal images generated by ultrasound radio frequency (RF) data. The ultrasound RF data contain information related to the arrangement of scatterers in tissues and organs, which is only partially available in B-mode images. To solve this problem, we use ultrasound RF data to generate parametric images, including Nakagami and entropy images, to illustrate the abundant backscattered statistics contained in RF data. A channel-aware fusion module is proposed to adaptively fuse the features from images of different modalities. Experimental results show that the abundant information contained in RF data can be helpful to identify thyroid glands or breast lesions with low contrast and fuzzy boundaries in B-mode ultrasound images and improve the segmentation performance.

Medical ultrasound image segmentation using Multi-Residual U-Net architecture

Medical ultrasound image segmentation using Multi-Residual U-Net architecture

Intelligent contour extraction approach for accurate segmentation of medical ultrasound images.

Introduction: Accurate contour extraction in ultrasound images is of great interest for image-guided organ interventions and disease diagnosis. Nevertheless, it remains a problematic issue owing to the missing or ambiguous outline between organs (i.e., prostate and kidney) and surrounding tissues, the appearance of shadow artifacts, and the large variability in the shape of organs. Methods: To address these issues, we devised a method that includes four stages. In the first stage, the data sequence is acquired using an improved adaptive selection principal curve method, in which a limited number of radiologist defined data points are adopted as the prior. The second stage then uses an enhanced quantum evolution network to help acquire the optimal neural network. The third stage involves increasing the precision of the experimental outcomes after training the neural network, while using the data sequence as the input. In the final stage, the contour is smoothed using an explicable mathematical formula explained by the model parameters of the neural network. Results: Our experiments showed that our approach outperformed other current methods, including hybrid and Transformer-based deep-learning methods, achieving an average Dice similarity coefficient, Jaccard similarity coefficient, and accuracy of 95.7 ± 2.4%, 94.6 ± 2.6%, and 95.3 ± 2.6%, respectively. Discussion: This work develops an intelligent contour extraction approach on ultrasound images. Our approach obtained more satisfactory outcome compared with recent state-of-the-art approaches . The knowledge of precise boundaries of the organ is significant for the conservation of risk structures. Our developed approach has the potential to enhance disease diagnosis and therapeutic outcomes.

A hybrid enhanced attention transformer network for medical ultrasound image segmentation

Ultrasound (US) technology is a widely utilized for clinical screening owing to its cost-effectiveness, painlessness, and convenience. However, the automatic segmentation of lesions or organs in US images remains to be challenging due to speckle artifacts, blurred boundaries, and low contrast. Recently, transformer-based methods have shown to be effective in long-range dependency, which is a good complement to Convolutional Neural Networks (CNN). In this article, we present a novel U-shape segmentation model based on a hybrid CNN-transformer structure that can effectively integrate CNN local features and transformer long-range contextual information of US images. To begin with, we design a coordinate residual block (CdRB) to encode the absolute position information of lesions. Further, we develop a channel enhanced self-attention-based transformer (ECAT) to help enhance the response of extracted global features. Finally, we adopt a comprehensive dual attention module (CDAM) to enhance skip connection features, which can learn feature correlations and capture more accurate edge features in US images. Results based on four public US datasets demonstrate that our method outperforms state-of-the-art segmentation methods, with 0.741 Dice on BUSI for breast lesion segmentation, 0.827 Dice on DDTI for thyroid lesion segmentation, 0.895 Dice on TN3k for thyroid lesion segmentation and 0.940 Dice on CAMUS for left ventricle segmentation. Furthermore, the robustness of our network is further demonstrated by an external validation dataset for breast lesion segmentation. In summary, our method showcases excellent adaptability and robustness in US image segmentation and can potentially be a general US segmentation tool.

Rethinking the unpretentious U-net for medical ultrasound image segmentation

Breast tumor segmentation from ultrasound images is one of the key steps that help us characterize and localize tumor regions. However, variable tumor morphology, blurred boundaries, and similar intensity distributions bring challenges for radiologists to segment breast tumors manually. During clinical diagnosis, there are higher demands on the segmentation accuracy and efficiency of breast ultrasound images, so there is an urgent need for an automated method to improve the segmentation accuracy as a technical tool to assist diagnosis. Inspired by the U-net and its many variations, this paper proposed an unpretentious nested U-net (NU-net) for accurate and efficient breast tumor segmentation. The key idea is to utilize U-nets with different depths and shared weights to achieve robust characterization of breast tumors. Specifically, we first utilize the deeper U-net (fifteen layers) as the backbone network to extract more sufficient breast tumor features. Then, we developed a multi-output U-net to be taken as the bond between the encoder and the decoder to enhance the network adaptability for breast tumors with different scales. Finally, the short-connection based on multi-step down-sampling is used to enhance the correlation of long-range information of encoded features. Extensive experimental results with fifteen state-of-the-art segmentation methods on three public breast ultrasound datasets demonstrate that our method has a more competitive segmentation performance on breast tumors. Furthermore, the robustness of our approach is further illustrated by the segmentation of renal ultrasound images. The source code is publicly available on https://github.com/CGPxy/NU-net.

DSEU-net: A novel deep supervision SEU-net for medical ultrasound image segmentation

The automatic and accurate medical ultrasound image segmentation has been a challenging task due to the coupled interference of various internal and external factors. In recent years, CNN techniques have been widely and successfully used in medical image segmentation. Motivated by this, this paper proposes a novel squeeze-and-excitation attention U-net with deep supervision (DSEU-net) for medical ultrasound image segmentation. Specifically, a deeper U-net is first used as a benchmark network to capture sufficient target feature information from complex ultrasound images. Then, the squeeze-and-excitation (SE) block is regarded as the bond between encoder and decoder to enhance the attention to useful object regions. Moreover, the introduction of SE block not only strengthens the association of useful information at a distance, but also suppresses the introduction of irrelevant information. Finally, the deep supervised constraints are added to the decoding stage of the network to refine the prediction masks of ultrasound images. Extensive experimental results on three clinical ultrasound datasets show that DSEU-net has better robustness and superiority in ultrasound image segmentation. In the segmentation of the first breast ultrasound dataset (BUSI), the values of Jaccard, Precision, Recall, Specificity and Dice are 70.36%, 79.73%, 82.70%, 97.42% and 78.51%, respectively. The values of Jaccard, Precision, Recall, Specificity and Dice for our method on the second breast ultrasound dataset (Dataset B) are 73.17%, 82.58%, 84.02%, 99.05% and 81.50%, respectively. For the segmentation of kidney ultrasound dataset (KUS), the values of Jaccard, Precision, Recall, Specificity, Dice, HD, ASSD and ABD are 89.47, 94.77, 94.36, 99.10, 94.32, 12.42, 0.48 and 3.44, respectively. Comparing with the original U-net, DSEU-net improved on average 8.28% and 12.55% on five metrics for two breast ultrasound data. DSEU-net improved on average 54.81% on eight metrics for the kidney ultrasound dataset.

Medical Ultrasound Image Segmentation With Deep Learning Models

This work aimed to adopt a transformer model combined with deep learning neural network to discuss the segmentation of medical ultrasound images. A network combining a transformer model with a deep neural network model (ConvTrans-Net) is proposed. The image content is preselected based on a multilayer perceptron and attention mechanism, different feature vectors are concatenated and fed into the multilayer perceptron, and the results of multiple attentions are mapped to a larger dimensional space using a feed-forward network. The lesion areas segmented by ultrasonic scan were analysed, and an attention mechanism and multilayer perceptron were combined to preselect image content. The performance and convergence of the model were analysed, and the Jaccard similarity coefficient precision and recall of the model were measured. In the experiment, two different iterative step sizes were selected, the convergence trend of the model increased with the increase in the number of iterative steps, and the model gradually stabilized. The Jaccard of ConvTrans-Net was 85.21%. The precision (85.17%) and recall (89.65%) were significantly higher than those of EfficientNet and DeepViT-L, and the differences were significant (P < 0.05). The experimental results show that the proposed model is stable, and the combination of Transformer model and deep learning neural network has a good effect on ultrasound image segmentation, which has some practical application value.

Medical ultrasound image segmentation using genetic active contour

Image segmentation is one of the earliest and most important stages of image processing and plays an important role in both qualitative and quantitative analysis of medical ultrasound images but ultrasound images have low level of contrast and are corrupted with strong speckle noise. Due to these effects, segmentation of ultrasound images is very challenging and traditional image segmentation methods may not be leads to satisfactory results. The active contour method has been one of the widely used techniques for image segmentation; however, due to low quality of ultrasound images, it has encountered difficulties. In this paper, we presented a segmental method combined genetic algorithm and active contour with an energy minimization procedure based on genetic algorithms. This method have been proposed to overcome some limits of classical active contours, as con-tour initialization and local minima (speckle noise), and have been successfully applied on medical ultrasound images. Experimental result on medical ultrasound image show that our presented method only can correctly segment the circular tissue’s on ultra-sound images.

Medical ultrasound image segmentation by modified local histogram range image method

Fast and satisfied medical ultrasound segmentation is known to be difficult due to speckle noises and other artificial effects. Since speckle noise is formed from random signals which are emitted by an ultrasound system, we can’t encounter the same way as other image noises. Lack of information in ultrasound images is another problem. Thus, segmentation results may not be accurate enough by means of customary image segmentation methods. Those methods that can specify undesirable effects and segment them by eliminating artificial effects, should be chosen. It seems to be a complicated work with high computational load. The current study presents a different approach to ultrasound image segmentation that relies mainly on local evaluation, named as local histogram range image method which is modified by means of discrete wavelet transform. Thus, a significant decrease in computational load is then achieved. The results show that it is possible for tissues to be segmented correctly.