Global guidance network for breast lesion segmentation in ultrasound images

Xue, Cheng; Zhu, Lei; Fu, Huazhu; Hu, Xiaowei; Li, Yongming; Zhang, Hai; Heng, Pheng‐Ann

doi:10.1016/j.media.2021.101989

Cited by 102 publications

(17 citation statements)

References 56 publications

(152 reference statements)

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“…To better address the lesion segmentation task, authors have focused on developing new or refining already existing algorithms [ 19 , 20 ]. For example, Xue et al proposed a deep convolutional neural network equipped with a global guidance block that enhances breast lesion segmentation by utilizing the broad contextual features of US images [ 21 ]. Nonetheless, from the clinical perspective, detailed lesion segmentation, however, is not required to diagnose breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Magnuska

Theek

Darguzyte

et al. 2022

Cancers

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Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola–Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola–Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.

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Section: Introductionmentioning

confidence: 99%

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Magnuska

Theek

Darguzyte

et al. 2022

Cancers

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show abstract

“…Concatenate and convolution operations are performed to achieve multi-scale feature fusion and generate multi-scale feature maps. We also incorporated a GGB module ( Xue et al, 2021 ) that integrates spatial and channel attention through which long-range feature dependencies and contextual scale information are learned. Our UGBNet can integrate deep and shallow features to generate multi-level synthetic features as the spatial and channel-wise guiding information of non-local blocks ( Chen L et al, 2017 ) and to complement the edge details that are usually ignored by deep CNNs.…”

Section: Methodsmentioning

confidence: 99%

Novel automated spinal ultrasound segmentation approach for scoliosis visualization

Jiang

Fang²,

Xie³

2022

Front. Physiol.

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Scoliosis is a 3D deformity of the spine in which one or more segments of the spine curve laterally, usually with rotation of the vertebral body. Generally, having a Cobb angle (Cobb) greater than 10° can be considered scoliosis. In spine imaging, reliable and accurate identification and segmentation of bony features are crucial for scoliosis assessment, disease diagnosis, and treatment planning. Compared with commonly used X-ray detection methods, ultrasound has received extensive attention from researchers in the past years because of its lack of radiation, high real-time performance, and low price. On the basis of our previous research on spinal ultrasound imaging, this work combines artificial intelligence methods to create a new spine ultrasound image segmentation model called ultrasound global guidance block network (UGBNet), which provides a completely automatic and reliable spine segmentation and scoliosis visualization approach. Our network incorporates a global guidance block module that integrates spatial and channel attention, through which long-range feature dependencies and contextual scale information are learned. We evaluate the performance of the proposed model in semantic segmentation on spinal ultrasound datasets through extensive experiments with several classical learning segmentation methods, such as UNet. Results show that our method performs better than other approaches. Our UGBNet significantly improves segmentation precision, which can reach 74.2% on the evaluation metric of the Dice score.

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“…This further paves the way for development of AI detection and evaluation of cancer changes, such as temporal subtraction where the changes can be detected at different time intervals. Previous research also showed the superior performance of AI algorithms [ 2 , 3 , 4 ]. The registration of PET and MRI remains challenging for three reasons: (1) the patient position is different during PET and MR scanning.…”

Section: Introductionmentioning

confidence: 94%

Multimodal Patient-Specific Registration for Breast Imaging Using Biomechanical Modeling with Reference to AI Evaluation of Breast Tumor Change

Xue

Tang

Lai

et al. 2021

Life

Self Cite

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Background: The strategy to combat the problem associated with large deformations in the breast due to the difference in the medical imaging of patient posture plays a vital role in multimodal medical image registration with artificial intelligence (AI) initiatives. How to build a breast biomechanical model simulating the large-scale deformation of soft tissue remains a challenge but is highly desirable. Methods: This study proposed a hybrid individual-specific registration model of the breast combining finite element analysis, property optimization, and affine transformation to register breast images. During the registration process, the mechanical properties of the breast tissues were individually assigned using an optimization process, which allowed the model to become patient specific. Evaluation and results: The proposed method has been extensively tested on two datasets collected from two independent institutions, one from America and another from Hong Kong. Conclusions: Our method can accurately predict the deformation of breasts from the supine to prone position for both the Hong Kong and American samples, with a small target registration error of lesions.

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Global guidance network for breast lesion segmentation in ultrasound images

Cited by 102 publications

References 56 publications

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Novel automated spinal ultrasound segmentation approach for scoliosis visualization

Multimodal Patient-Specific Registration for Breast Imaging Using Biomechanical Modeling with Reference to AI Evaluation of Breast Tumor Change

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