Evolutionary pulse-coupled neural network for segmenting breast lesions on ultrasonography

BUS-BRA: A breast ultrasound dataset for assessing computer-aided diagnosis systems

PURPOSE

Computer-aided diagnosis (CAD) systems on breast ultrasound (BUS) aim to increase the efficiency and effectiveness of breast screening, helping specialists to detect and classify breast lesions. CAD system development requires a set of annotated images, including lesion segmentation, biopsy results to specify benign and malignant cases, and BI-RADS categories to indicate the likelihood of malignancy. Besides, standardized partitions of training, validation, and test sets promote reproducibility and fair comparisons between different approaches. Thus, we present a publicly available BUS dataset whose novelty is the substantial increment of cases with the above-mentioned annotations and the inclusion of standardized partitions to objectively assess and compare CAD systems.

ACQUISITION AND VALIDATION METHODS

The BUS dataset comprises 1875 anonymized images from 1064 female patients acquired via four ultrasound scanners during systematic studies at the National Institute of Cancer (Rio de Janeiro, Brazil). The dataset includes biopsy-proven tumors divided into 722 benign and 342 malignant cases. Besides, a senior ultrasonographer performed a BI-RADS assessment in categories 2 to 5. Additionally, the ultrasonographer manually outlined the breast lesions to obtain ground truth segmentations. Furthermore, 5- and 10-fold cross-validation partitions are provided to standardize the training and test sets to evaluate and reproduce CAD systems. Finally, to validate the utility of the BUS dataset, an evaluation framework is implemented to assess the performance of deep neural networks for segmenting and classifying breast lesions.

DATA FORMAT AND USAGE NOTES

The BUS dataset is publicly available for academic and research purposes through an open-access repository under the name BUS-BRA: A Breast Ultrasound Dataset for Assessing CAD Systems. BUS images and reference segmentations are saved in Portable Network Graphic (PNG) format files, and the dataset information is stored in separate Comma-Separated Value (CSV) files.

POTENTIAL APPLICATIONS

The BUS-BRA dataset can be used to develop and assess artificial intelligence-based lesion detection and segmentation methods, and the classification of BUS images into pathological classes and BI-RADS categories. Other potential applications include developing image processing methods like despeckle filtering and contrast enhancement methods to improve image quality and feature engineering for image description.

A Robust Breast ultrasound segmentation method under noisy annotations

BACKGROUND AND OBJECTIVE

A large-scale training data and accurate annotations are fundamental for current segmentation networks. However, the characteristic artifacts of ultrasound images always make the annotation task complicated, such as attenuation, speckle, shadows and signal dropout. Further complications arise as the contrast between the region of interest and background is often low. Without double-check from professionals, it is hard to guarantee that there is no noisy annotation in segmentation datasets. However, among the deep learning methods applied to ultrasound segmentation so far, no one can solve this problem.

METHOD

Given a dataset with poorly labeled masks, including a certain amount of noises, we propose an end-to-end noisy annotation tolerance network (NAT-Net). NAT-Net can detect noise by the proposed noise index (NI) and dynamically correct noisy annotations in the training stage. Simultaneously, noise index is used to correct the noise along with the output of the learning model. This method does not need any auxiliary clean datasets or prior knowledge of noise distributions, so it is more general, robust and easier to apply than the existing methods.

RESULTS

NAT-Net outperforms previous state-of-the-art methods on synthesized data with different noise ratio. For real-world dataset with more complex noise types, the IoU of NAT-Net is higher than that of state-of-art approaches by nearly 6%. Experimental results show that our method can also achieve good results compared with the existing methods for clean dataset.

CONCLUSION

The NAT-Net reduces manual interaction of data annotation, reduces dependence on medical personnel. After tumor segmentation, disease diagnosis efficiency is improved, which provides an auxiliary strategies for subsequent medical diagnosis systems based on ultrasound.

Healthcare

Quality analysis of the treatment of cancer has been an objective of e-health services for quite some time. The objective is to predict the stage of breast cancer by using diverse input parameters. Breast cancer is one of the main causes of death in women when compared to other tumors. The classification of breast cancer information can be profitable to anticipate diseases or track the hereditary of tumors. For classification, an artificial neural network (ANN) structure was carried out. In the structure, nine training algorithms are used and the proposed is the Levenberg-Marquardt algorithm. For optimizing the hidden layer and neuron, three optimization techniques are used. In the result, the best approval execution is anticipated and the diverse execution evaluation estimation for three optimization algorithms is researched. The correlation execution diagram for an accuracy of 95%, a sensitivity of 98%, and a specificity of 89% of a social spider optimization (SSO) algorithm are shown.