AWDS-net: automatic whole-field segmentation network for characterising diverse breast masses

Jiao, Jiajia; Chen, Yingzhao; Li, Zhiyu; Weng, Tien-Hsiung

doi:10.1080/09540091.2023.2289836

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…Despite all recent advancements, how to capture finer local details to facilitate accurate delineation of mass boundaries remains a significant challenge for the global segmentation approach due to the interference of complex image backgrounds, blurred mass margins and mass size variations (Wang et al 2021, Chowdary and Yoagarajah 2023, Jiao et al 2024. To capture finer local details, some studies adopted a local segmentation approach that segments masses from the detected mass regions of interest (ROIs).…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based method for the detection and segmentation of breast masses in ultrasound images

Li,

Ye,

Chen

et al. 2024

Phys. Med. Biol.

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Objective. Automated detection and segmentation of breast masses in ultrasound images are critical for breast cancer diagnosis, but remain challenging due to limited image quality and complex breast tissues. This study aims to develop a deep learning-based method that enables accurate breast mass detection and segmentation in ultrasound images.Approach. A novel convolutional neural network-based framework that combines the You Only Look Once (YOLO) v5 network and the Global-Local (GOLO) strategy was developed. First, YOLOv5 was applied to locate the mass regions of interest (ROIs). Second, a Global Local-Connected Multi-Scale Selection (GOLO-CMSS) network was developed to segment the masses. The GOLO-CMSS operated on both the entire images globally and mass ROIs locally, and then integrated the two branches for a final segmentation output. Particularly, in global branch, CMSS applied Multi-Scale Selection (MSS) modules to automatically adjust the receptive fields, and Multi-Input (MLI) modules to enable fusion of shallow and deep features at different resolutions. The USTC dataset containing 28,477 breast ultrasound images was collected for training and test. The proposed method was also tested on three public datasets, UDIAT, BUSI and TUH. The segmentation performance of GOLO-CMSS was compared with others networks and three experienced radiologists.Main results. YOLOv5 outperformed other detection models with average precisions of 99.41%, 95.15%, 93.69% and 96.42% on the USTC, UDIAT, BUSI and TUH datasets, respectively. The proposed GOLO-CMSS showed superior segmentation performance over other state-of-the-art networks, with Dice similarity coefficients (DSCs) of 93.19%, 88.56%, 87.58% and 90.37% on the USTC, UDIAT, BUSI and TUH datasets, respectively. The mean DSC between GOLO-CMSS and each radiologist was significantly better than that between radiologists (p < 0.001).Significance. Our proposed method can accurately detect and segment breast masses with a decent performance comparable to radiologists, highlighting its great potential for clinical implementation in breast ultrasound examination.

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Section: Introductionmentioning

confidence: 99%

A deep learning-based method for the detection and segmentation of breast masses in ultrasound images

Li,

Ye,

Chen

et al. 2024

Phys. Med. Biol.

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Segmentation and Classification of Breast Masses From the Whole Mammography Images Using Transfer Learning and BI‐RADS Characteristics

Oudjer,

Cherfa,

Cherfa

et al. 2024

Int J Imaging Syst Tech

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Breast cancer is the most prevalent cancer among women worldwide, highlighting the critical need for its accurate detection and early diagnosis. In this context, the segmentation of breast masses (the most common symptom of breast cancer) plays a crucial role in analyzing mammographic images. In addition, in image processing, the analysis of mammographic images is very common, but certain combinations of mathematical tools have never been exploited. We propose a computer‐aided diagnosis (CAD) system designed with different and new algorithm combinations for the segmentation and classification of breast masses based on the Breast Imaging‐Reporting and Data System (BI‐RADS) lexicon. The image is initially divided into superpixels using the simple linear iterative clustering (SLIC) algorithm. Fine‐tuning of ResNet50, EfficientNetB2, MobileNetV2, and InceptionV3 models is employed to extract features from superpixels. The classification of each superpixel as background or breast mass is performed by feeding the extracted features into a support vector machine (SVM) classifier, resulting in an accurate primary segmentation for breast masses, refined by the GrabCut algorithm with automated initialization. Finally, we extract contour, texture, and shape parameters from the segmented regions for the classification of masses into BI‐Rads 2, 3, 4, and 5 using the gradient boost (GB) classifier while also examining the impact of the surrounding tissue. The proposed method was evaluated on the INBreast database, achieving a Dice score of 87.65% and a sensitivity of 87.96% for segmentation. For classification, we obtained a sensitivity of 88.66%, a precision of 90.51%, and an area under the curve (AUC) of 97.8%. The CAD system demonstrates high accuracy in both the segmentation and classification of breast masses, providing a reliable tool for aiding breast cancer diagnosis using the BI‐Rads lexicon. The study also showed that the surrounding tissue has an impact on classification, leading to the importance of choosing the right size of ROIs.

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AWDS-net: automatic whole-field segmentation network for characterising diverse breast masses

Cited by 2 publications

References 28 publications

A deep learning-based method for the detection and segmentation of breast masses in ultrasound images

A deep learning-based method for the detection and segmentation of breast masses in ultrasound images

Segmentation and Classification of Breast Masses From the Whole Mammography Images Using Transfer Learning and BI‐RADS Characteristics

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