Speckle Reduction via Deep Content-Aware Image Prior for Precise Breast Tumor Segmentation in an Ultrasound Image

A multi-task self-supervised approach for mass detection in automated breast ultrasound using double attention recurrent residual U-Net

Breast cancer is the most common and lethal cancer among women worldwide. Early detection using medical imaging technologies can significantly improve treatment outcomes. Automated breast ultrasound, known as ABUS, offers more advantages compared to traditional mammography and has recently gained considerable attention. However, reviewing hundreds of ABUS slices imposes a high workload on radiologists, increasing review time and potentially leading to diagnostic errors. Consequently, there is a strong need for efficient computer-aided detection, CADe, systems. In recent years, researchers have proposed deep learning-based CADe systems to enhance mass detection accuracy. However, these methods are highly dependent on the number of training samples and often struggle to balance detection accuracy with the false positive rate. To reduce the workload for radiologists and achieve high detection sensitivities with low false positive rates, this study introduces a novel CADe system based on a self-supervised framework that leverages unannotated ABUS datasets to improve detection results. The proposed framework is integrated into an innovative 3-D convolutional neural network called DATTR2U-Net, which employs a multi-task learning approach to simultaneously train inpainting and denoising pretext tasks. A fully convolutional network is then attached to the DATTR2U-Net for the detection task. The proposed method is validated on the TDSCABUS public dataset, demonstrating promising detection results with a recall of 0.7963 and a false positive rate of 5.67 per volume that signifies its potential to improve detection accuracy while reducing workload for radiologists. The code is available at: github.com/Pooryamn/SSL_ABUS.

A deep supervised transformer U-shaped full-resolution residual network for the segmentation of breast ultrasound image

PURPOSE

Breast ultrasound (BUS) is an important breast imaging tool. Automatic BUS image segmentation can measure the breast tumor size objectively and reduce doctors' workload. In this article, we proposed a deep supervised transformer U-shaped full-resolution residual network (DSTransUFRRN) to segment BUS images.

METHODS

In the proposed method, a full-resolution residual stream and a deep supervision mechanism were introduced into TransU-Net. The residual stream can keep full resolution features from different levels and enhance features fusion. Then, the deep supervision can suppress gradient dispersion. Moreover, the transformer module can suppress irrelevant features and improve feature extraction process. Two datasets (dataset A and B) were used for training and evaluation. The dataset A included 980 BUS image samples and the dataset B had 163 BUS image samples.

RESULTS

Cross-validation was conducted. For the dataset A, the proposed DSTransUFRRN achieved significantly higher Dice (91.04 ± 0.86%) than all compared methods (p < 0.05). For the dataset B, the Dice was lower than that for the dataset A due to the small number of samples, but the Dice of DSTransUFRRN (88.15% ± 2.11%) was significantly higher than that of other compared methods (p < 0.05).

CONCLUSIONS

In this study, we proposed DSTransUFRRN for BUS image segmentation. The proposed methods achieved significantly higher accuracy than the compared previous methods.

A despeckling method for ultrasound images utilizing content-aware prior and attention-driven techniques

The despeckling of ultrasound images contributes to the enhancement of image quality and facilitates precise treatment of conditions such as tumor cancers. However, the use of existing methods for eliminating speckle noise can cause the loss of image texture features, impacting clinical judgment. Thus, maintaining clear lesion boundaries while eliminating speckle noise is a challenging task. This paper presents an innovative approach for denoising ultrasound images using a novel noise reduction network model called content-aware prior and attention-driven (CAPAD). The model employs a neural network to automatically capture the hidden prior features in ultrasound images to guide denoising and embeds the denoiser into the optimization module to simultaneously optimize parameters and noise. Moreover, this model incorporates a content-aware attention module and a loss function that preserves the structural characteristics of the image. These additions enhance the network's capacity to capture and retain valuable information. Extensive qualitative evaluation and quantitative analysis performed on a comprehensive dataset provide compelling evidence of the model's superior denoising capabilities. It excels in noise suppression while successfully preserving the underlying structures within the ultrasound images. Compared to other denoising algorithms, it demonstrates an improvement of approximately 5.88% in PSNR and approximately 3.61% in SSIM. Furthermore, using CAPAD as a preprocessing step for breast tumor segmentation in ultrasound images can greatly improve the accuracy of image segmentation. The experimental results indicate that the utilization of CAPAD leads to a notable enhancement of 10.43% in the AUPRC for breast cancer tumor segmentation.