Automated diagnosis of bone metastasis based on multi-view bone scans using attention-augmented deep neural networks

Pi, Yong; Zhen, Zhao; Xiang, Yongzhao; Li, Yuhao; Cai, Huawei; Zhang, Yi

doi:10.1016/j.media.2020.101784

Cited by 55 publications

(38 citation statements)

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“…Since 2012, when AlexNet 13 won the 2012 ILSVRC competition, 14 numerous important breakthroughs in computer vision have been achieved using DCNNs 15‐20 . Benefit from the development of DCNNs, continuous optimization of object detection algorithms in natural images, and the release of open‐source medical image datasets, the studies on object detection in medical images have made significant progress.…”

Section: Related Workmentioning

confidence: 99%

“…Since 2012, when AlexNet 13 won the 2012 ILSVRC competition, 14 numerous important breakthroughs in computer vision have been achieved using DCNNs. [15][16][17][18][19][20] Benefit from the development of DCNNs, continuous optimization of object detection algorithms in natural images, and the release of open-source medical image datasets, the studies on object detection in medical images have made significant progress. According to the different dimensions of the medical data used in them, these studies can be divided into two-dimensional (2D) detection and three-dimensional (3D) detection.…”

Section: Object Detection In Medical Imagesmentioning

confidence: 99%

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An intelligent system of pelvic lymph node detection

et al. 2021

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Computed tomography (CT) scanning is a fast and painless procedure that can capture clear imaging information beneath the abdomen and is widely used to help diagnose and monitor disease progress. The pelvic lymph node is a key indicator of colorectal cancer metastasis. In the traditional process, an experienced radiologist must read all the CT scanning images slice by slice to track the lymph nodes for future diagnosis. However, this process is time‐consuming, exhausting, and subjective due to the complex pelvic structure, numerous blood vessels, and small lymph nodes. Therefore, automated methods are desirable to make this process easier. Currently, the available open‐source CTLNDataset only contains large lymph nodes. Consequently, a new data set called PLNDataset, which is dedicated to lymph nodes within the pelvis, is constructed to solve this issue. A two‐level annotation calibration method is proposed to guarantee the quality and correctness of pelvic lymph node annotation. Moreover, a novel system composed of a keyframe localization network and a lymph node detection network is proposed to detect pelvic lymph nodes in CT scanning images. The proposed method makes full use of two kinds of prior knowledge: spatial prior knowledge for keyframe localization and anchor prior knowledge for lymph node detection. A series of experiments are carried out to evaluate the proposed method, including ablation experiments, comparing other state‐of‐the‐art methods, and visualization of results. The experimental results demonstrate that our proposed method outperforms other methods on PLNDataset and CTLNDataset. This system is expected to be applied in future clinical practice.

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Section: Related Workmentioning

confidence: 99%

Section: Object Detection In Medical Imagesmentioning

confidence: 99%

An intelligent system of pelvic lymph node detection

et al. 2021

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“…Owe to its high disease sensitivity, SPECT scintigraphy has attracted attention from the field of computer-aided diagnosis/detection. Specifically, the automated models were developed to classify SPECT scintigraphic images using deep learning algorithms [3][4][5][6][7][8][9]. 2D SPECT scintigraphy is characterized by low specificity mainly caused by the inferior planar spatial resolution, which brings a significant challenge to a manual analysis by physicians for the diagnosis of bone metastasis and other diseases.…”

Section: Introductionmentioning

confidence: 99%

“…Convolutional neural network (CNN) as the mainstream of deep learning techniques has been exploited to develop automated classification models by leveraging their superior capability of automatically extracting features from images at different levels in an optimal way. Existing work mainly focuses on the development of CNN-based automated classification models for identifying bone lesions metastasized from multiple primary solid tumors [3,4], prostate cancer [5,[7][8][9], and breast cancer [6]. In our previous work, we developed CNN-based models to identify bone metastasis with thoracic SPECT scintigraphic images [12] and to segment the metastasized lesions from thoracic SPECT scintigraphic images [13].…”

Section: Introductionmentioning

confidence: 99%

Multiclass classification of whole‐body scintigraphic images using a self‐defined convolutional neural network with attention modules

Lin

Cao

et al. 2021

Medical Physics

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Purpose: A self-defined convolutional neural network is developed to automatically classify whole-body scintigraphic images of concern (i.e., the normal, metastasis, arthritis, and thyroid carcinoma), automatically detecting diseases with whole-body bone scintigraphy.Methods: A set of parameter transformation operations are first used to augment the original dataset of whole-body bone scintigraphic images. A hybrid attention mechanism including the spatial and channel attention module is then introduced to develop a deep classification network, Dscint, which consists of eight This article is protected by copyright. All rights reserved.weight layers, one hybrid attention module, two normalization modules, two fully connected layers, and one softmax layer.Results. Experimental evaluations conducted on a set of whole-body scintigraphic images show that the proposed deep classification network, Dscint, performs well for automated detection of diseases by classifying the images of concerns, with achieving the accuracy, precision, recall, specificity, and F-1 score of 0.9801, 0.9795, 0.9791, 0.9933, and 0.9792, respectively, on the test data in the augmented dataset. A comparative analysis of Dscint and several classical deep classification networks (i.e., AlexNet, ResNet, VGGNet, DenseNet, and Inception-v4) reveals that our self-defined network, Dscint, performs best on classifying whole-body scintigraphic images on the same dataset. Conclusions:The self-defined deep classification network, Dscint, can be utilized to automatically determine whether a whole-body scintigraphic image either is normal or contains diseases of concerns.Specifically, better performance of Dscint is obtained on images with lesions that are present in relatively fixed locations like thyroid carcinoma than those with lesions occurring in non-fixed locations of bone tissue.

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“…Previously, we had proposed an automated diagnostic system of bone metastasis based on multi-view bone scans using an attention-augmented deep neural network [ 14 , 15 ]. While it achieved considerable accuracy in the patient-based diagnosis from WBS images, a definitive diagnosis for suspicious bone metastatic lesions is still crucial for pragmatic decisions, such as precise bone biopsy, bone surgery and external beam radiotherapy [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network

Liu

Yang

et al. 2021

BMC Med Imaging

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Background We aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone scintigraphy (WBS) images by convolutional neural networks (CNNs). Methods We retrospectively collected the 99mTc-MDP WBS images with confirmed bone lesions from 3352 patients with malignancy. 14,972 bone lesions were delineated manually by physicians and annotated as benign and malignant. The lesion-based differentiating performance of the proposed network was evaluated by fivefold cross validation, and compared with the other three popular CNN architectures for medical imaging. The average sensitivity, specificity, accuracy and the area under receiver operating characteristic curve (AUC) were calculated. To delve the outcomes of this study, we conducted subgroup analyses, including lesion burden number and tumor type for the classifying ability of the CNN. Results In the fivefold cross validation, our proposed network reached the best average accuracy (81.23%) in identifying suspicious bone lesions compared with InceptionV3 (80.61%), VGG16 (81.13%) and DenseNet169 (76.71%). Additionally, the CNN model's lesion-based average sensitivity and specificity were 81.30% and 81.14%, respectively. Based on the lesion burden numbers of each image, the area under the receiver operating characteristic curve (AUC) was 0.847 in the few group (lesion number n ≤ 3), 0.838 in the medium group (n = 4–6), and 0.862 in the extensive group (n > 6). For the three major primary tumor types, the CNN-based lesion identifying AUC value was 0.870 for lung cancer, 0.900 for prostate cancer, and 0.899 for breast cancer. Conclusion The CNN model suggests potential in identifying suspicious benign and malignant bone lesions from whole-body bone scintigraphic images.

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Automated diagnosis of bone metastasis based on multi-view bone scans using attention-augmented deep neural networks

Cited by 55 publications

References 20 publications

An intelligent system of pelvic lymph node detection

An intelligent system of pelvic lymph node detection

Multiclass classification of whole‐body scintigraphic images using a self‐defined convolutional neural network with attention modules

Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network

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