Enhancing Breast Ultrasound Segmentation through Fine-tuning and Optimization Techniques: Sharp Attention UNet

Abstract

Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it a popular choice among researchers in the medical image segmentation field. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the dice coefficient, specificity, sensitivity, and F1 score obtained values of 0.9283, 0.9936, 0.9426, and 0.9412, respectively. In addition, McNemar's statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperforms the earlier designed models and points towards improved breast lesion segmentation algorithms.

WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound

“…The results obtained from the WATUNet algorithm demonstrate an improvement in the sensitivity of lesion area mask extraction for the VSI dataset, with a notable increase of 1.6% compared to the second-best model. The Dice coefficient is also shown to have improved by 1.1% when compared to the second-best model [44]. These findings suggest that WATUNet may be a promising solution for improving the accuracy, robustness, and computational efficiency of lesion area segmentation in ultrasound imaging.…”

“…Compared to our previous work at [44], we focused on addressing the vanishing gradient during training. The attention mechanism emphasizes the overarching and highlighted information in an image, prioritizing salient and general details such as the shape of the tumor.…”

“…Similar to our previous model [44], in this study we applied contrast limited adaptive histogram equalization (CLAHE) [45], which is an image enhancement method that improves upon the adaptive Ultrasound probe sweeps are executed in various orientations, including transverse, sagittal, radial, and anti-radial. The picture is adopted from [5].…”

“…Both two activation functions exhibit negative curvature and smoothness, enabling efficient optimization and faster convergence in deep neural networks. We described each one of these activation functions in a previous work [44].…”

“…Table 1 presents the performance metrics obtained by five different neural network models, the backbone of these models are based on previous works namely; UNet [33], Attention UNet [39], Sharp UNet [38], Sharp Attention UNet [44] and the proposed WATUNet model on the test set of the VSI dataset. While the primary network architectures for the four former models have been previously proposed in the literature, the same backbone architecture and hyperparameters were employed for all four models when reporting the results in these tables.…”