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Chen YY, Yu PN, Lai YC, Hsieh TC, Cheng DC. Bone Metastases Lesion Segmentation on Breast Cancer Bone Scan Images with Negative Sample Training. Diagnostics (Basel) 2023; 13:3042. [PMID: 37835785 PMCID: PMC10572884 DOI: 10.3390/diagnostics13193042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/18/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023] Open
Abstract
The use of deep learning methods for the automatic detection and quantification of bone metastases in bone scan images holds significant clinical value. A fast and accurate automated system for segmenting bone metastatic lesions can assist clinical physicians in diagnosis. In this study, a small internal dataset comprising 100 breast cancer patients (90 cases of bone metastasis and 10 cases of non-metastasis) and 100 prostate cancer patients (50 cases of bone metastasis and 50 cases of non-metastasis) was used for model training. Initially, all image labels were binary. We used the Otsu thresholding method or negative mining to generate a non-metastasis mask, thereby transforming the image labels into three classes. We adopted the Double U-Net as the baseline model and made modifications to its output activation function. We changed the activation function to SoftMax to accommodate multi-class segmentation. Several methods were used to enhance model performance, including background pre-processing to remove background information, adding negative samples to improve model precision, and using transfer learning to leverage shared features between two datasets, which enhances the model's performance. The performance was investigated via 10-fold cross-validation and computed on a pixel-level scale. The best model we achieved had a precision of 69.96%, a sensitivity of 63.55%, and an F1-score of 66.60%. Compared to the baseline model, this represents an 8.40% improvement in precision, a 0.56% improvement in sensitivity, and a 4.33% improvement in the F1-score. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.
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Affiliation(s)
- Yi-You Chen
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 404, Taiwan; (Y.-Y.C.); (P.-N.Y.)
| | - Po-Nien Yu
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 404, Taiwan; (Y.-Y.C.); (P.-N.Y.)
| | - Yung-Chi Lai
- Department of Nuclear Medicine, Feng Yuan Hospital, Ministry of Health and Welfare, Taichung 420, Taiwan;
| | - Te-Chun Hsieh
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 404, Taiwan; (Y.-Y.C.); (P.-N.Y.)
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 404, Taiwan
| | - Da-Chuan Cheng
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 404, Taiwan; (Y.-Y.C.); (P.-N.Y.)
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2
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Segmentation of lung cancer-caused metastatic lesions in bone scan images using self-defined model with deep supervision. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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3
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Integrating Transfer Learning and Feature Aggregation into Self-defined Convolutional Neural Network for Automated Detection of Lung Cancer Bone Metastasis. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00770-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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4
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Parasca SV, Calin MA. Burn characterization using object-oriented hyperspectral image classification. JOURNAL OF BIOPHOTONICS 2022; 15:e202200106. [PMID: 35861489 DOI: 10.1002/jbio.202200106] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/10/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
This paper presents a new approach based on hyperspectral imaging combined with an object-oriented classification method that allows the generation of burn depth classification maps facilitating easier characterization of burns. Hyperspectral images of 14 patients diagnosed with burns on the upper and lower limbs were acquired using a pushbroom hyperspectral imaging system. The images were analyzed using an object-oriented classification approach that uses objects with specific spectral, textural and spatial attributes as the minimum unit for classifying information. The method performance was evaluated in terms of overall accuracy, sensitivity, precision and specificity computed from the confusion matrix. The results revealed that the approach proposed in this study performed well in differentiating burn classes with a high level of overall accuracy (95.99% ± 0.60%), precision (97.30% ± 2.46%), sensitivity (97.23% ± 3.02%) and specificity (98.02% ± 1.98%). In conclusion, the object-based approach for burns hyperspectral images classification can provide maps that can help surgeons identify with better precision different depths of burn wounds.
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Affiliation(s)
- Sorin Viorel Parasca
- Carol Davila University of Medicine and Pharmacy Bucharest, Bucharest, Romania
- Emergency Clinical Hospital for Plastic, Reconstructive Surgery and Burns, Bucharest, Romania
| | - Mihaela Antonina Calin
- National Institute of Research and Development for Optoelectronics-INOE 2000, Magurele, Romania
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Lin Q, Gao R, Luo M, Wang H, Cao Y, Man Z, Wang R. Semi-supervised segmentation of metastasis lesions in bone scan images. Front Mol Biosci 2022; 9:956720. [DOI: 10.3389/fmolb.2022.956720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 10/13/2022] [Indexed: 11/13/2022] Open
Abstract
To develop a deep image segmentation model that automatically identifies and delineates lesions of skeletal metastasis in bone scan images, facilitating clinical diagnosis of lung cancer–caused bone metastasis by nuclear medicine physicians. A semi-supervised segmentation model is proposed, comprising the feature extraction subtask and pixel classification subtask. During the feature extraction stage, cascaded layers which include the dilated residual convolution, inception connection, and feature aggregation learn the hierarchal representations of low-resolution bone scan images. During the pixel classification stage, each pixel is first classified into categories in a semi-supervised manner, and the boundary of pixels belonging to an individual lesion is then delineated using a closed curve. Experimental evaluation conducted on 2,280 augmented samples (112 original images) demonstrates that the proposed model performs well for automated segmentation of metastasis lesions, with a score of 0.692 for DSC if the model is trained using 37% of the labeled samples. The self-defined semi-supervised segmentation model can be utilized as an automated clinical tool to detect and delineate metastasis lesions in bone scan images, using only a few manually labeled image samples. Nuclear medicine physicians need only attend to those segmented lesions while ignoring the background when they diagnose bone metastasis using low-resolution images. More images of patients from multiple centers are typically needed to further improve the scalability and performance of the model via mitigating the impacts of variability in size, shape, and intensity of bone metastasis lesions.
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Guo Y, Lin Q, Zhao S, Li T, Cao Y, Man Z, Zeng X. Automated detection of lung cancer-caused metastasis by classifying scintigraphic images using convolutional neural network with residual connection and hybrid attention mechanism. Insights Imaging 2022; 13:24. [PMID: 35138479 PMCID: PMC8828823 DOI: 10.1186/s13244-022-01162-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 01/20/2022] [Indexed: 12/03/2022] Open
Abstract
Background Whole-body bone scan is the widely used tool for surveying bone metastases caused by various primary solid tumors including lung cancer. Scintigraphic images are characterized by low specificity, bringing a significant challenge to manual analysis of images by nuclear medicine physicians. Convolutional neural network can be used to develop automated classification of images by automatically extracting hierarchal features and classifying high-level features into classes. Results Using convolutional neural network, a multi-class classification model has been developed to detect skeletal metastasis caused by lung cancer using clinical whole-body scintigraphic images. The proposed method consisted of image aggregation, hierarchal feature extraction, and high-level feature classification. Experimental evaluations on a set of clinical scintigraphic images have shown that the proposed multi-class classification network is workable for automated detection of lung cancer-caused metastasis, with achieving average scores of 0.7782, 0.7799, 0.7823, 0.7764, and 0.8364 for accuracy, precision, recall, F-1 score, and AUC value, respectively. Conclusions The proposed multi-class classification model can not only predict whether an image contains lung cancer-caused metastasis, but also differentiate between subclasses of lung cancer (i.e., adenocarcinoma and non-adenocarcinoma). On the context of two-class (i.e., the metastatic and non-metastatic) classification, the proposed model obtained a higher score of 0.8310 for accuracy metric.
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Affiliation(s)
- Yanru Guo
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China
| | - Qiang Lin
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China. .,Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China. .,Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China.
| | - Shaofang Zhao
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China
| | - Tongtong Li
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China
| | - Yongchun Cao
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China
| | - Zhengxing Man
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China
| | - Xianwu Zeng
- Department of Nuclear Medicine, Gansu Provincial Tumor Hospital, Lanzhou, Gansu, China.
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Li T, Lin Q, Guo Y, Zhao S, Zeng X, Man Z, Cao Y, Hu Y. Automated detection of skeletal metastasis of lung cancer with bone scans using convolutional nuclear network. Phys Med Biol 2021; 67. [PMID: 34933282 DOI: 10.1088/1361-6560/ac4565] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 12/21/2021] [Indexed: 11/12/2022]
Abstract
Bone scan is widely used for surveying bone metastases caused by various solid tumors. Scintigraphic images are characterized by inferior spatial resolution, bringing a significant challenge to manual analysis of images by nuclear medicine physicians. We present in this work a new framework for automatically classifying scintigraphic images collected from patients clinically diagnosed with lung cancer. The framework consists of data preparation and image classification. In the data preparation stage, data augmentation is used to enlarge the dataset, followed by image fusion and thoracic region extraction. In the image classification stage, we use a self-defined convolutional neural network consisting of feature extraction, feature aggregation, and feature classification sub-networks. The developed multi-class classification network can not only predict whether a bone scan image contains bone metastasis but also tell which subcategory of lung cancer that a bone metastasis metastasized from is present in the image. Experimental evaluations on a set of clinical bone scan images have shown that the proposed multi-class classification network is workable for automated classification of metastatic images, with achieving average scores of 0.7392, 0.7592, 0.7242, and 0.7292 for accuracy, precision, recall, and F-1 score, respectively.
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Affiliation(s)
- Tongtong Li
- Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, Gansu, 730030, CHINA
| | - Qiang Lin
- School of Mathematics and Computer Science, Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, 730030, CHINA
| | - Yanru Guo
- Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, Gansu, 730030, CHINA
| | - Shaofang Zhao
- Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, Gansu, 730030, CHINA
| | - Xianwu Zeng
- Gansu Provincial Cancer Hospital, No. 2, Dongjie Rd., Lanzhou, Gansu, 730050, CHINA
| | - Zhengxing Man
- Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, Gansu, 730030, CHINA
| | - Yongchun Cao
- Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, Gansu, 730030, CHINA
| | - Yonghua Hu
- Gansu University of Chinese Medicine, No. 35, Dingxi Donglu Rd., Lanzhou, 730000, CHINA
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8
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Lin Q, Cao C, Li T, Man Z, Cao Y, Wang H. dSPIC: a deep SPECT image classification network for automated multi-disease, multi-lesion diagnosis. BMC Med Imaging 2021; 21:122. [PMID: 34380441 PMCID: PMC8359584 DOI: 10.1186/s12880-021-00653-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 07/27/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Functional imaging especially the SPECT bone scintigraphy has been accepted as the effective clinical tool for diagnosis, treatment, evaluation, and prevention of various diseases including metastasis. However, SPECT imaging is brightly characterized by poor resolution, low signal-to-noise ratio, as well as the high sensitivity and low specificity because of the visually similar characteristics of lesions between diseases on imaging findings. METHODS Focusing on the automated diagnosis of diseases with whole-body SPECT scintigraphic images, in this work, a self-defined convolutional neural network is developed to survey the presence or absence of diseases of concern. The data preprocessing mainly including data augmentation is first conducted to cope with the problem of limited samples of SPECT images by applying the geometric transformation operations and generative adversarial network techniques on the original SPECT imaging data. An end-to-end deep SPECT image classification network named dSPIC is developed to extract the optimal features from images and then to classify these images into classes, including metastasis, arthritis, and normal, where there may be multiple diseases existing in a single image. RESULTS A group of real-world data of whole-body SPECT images is used to evaluate the self-defined network, obtaining a best (worst) value of 0.7747 (0.6910), 0.7883 (0.7407), 0.7863 (0.6956), 0.8820 (0.8273) and 0.7860 (0.7230) for accuracy, precision, sensitivity, specificity, and F-1 score, respectively, on the testing samples from the original and augmented datasets. CONCLUSIONS The prominent classification performance in contrast to other related deep classifiers including the classical AlexNet network demonstrates that the built deep network dSPIC is workable and promising for the multi-disease, multi-lesion classification task of whole-body SPECT bone scintigraphy images.
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Affiliation(s)
- Qiang Lin
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, 730030, China. .,Key Lab of Streaming Data Computing and Applications, Northwest Minzu University, Lanzhou, 730030, China.
| | - Chuangui Cao
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, 730030, China.,Key Lab of Streaming Data Computing and Applications, Northwest Minzu University, Lanzhou, 730030, China
| | - Tongtong Li
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, 730030, China.,Key Lab of Streaming Data Computing and Applications, Northwest Minzu University, Lanzhou, 730030, China
| | - Zhengxing Man
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, 730030, China.,Key Lab of Streaming Data Computing and Applications, Northwest Minzu University, Lanzhou, 730030, China
| | - Yongchun Cao
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, 730030, China.,Key Lab of Streaming Data Computing and Applications, Northwest Minzu University, Lanzhou, 730030, China
| | - Haijun Wang
- Department of Nuclear Medicine, Gansu Provincial Hospital, Lanzhou, 730000, China
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