An Artificial Intelligence Multiprocessing Scheme for the Diagnosis of Osteosarcoma MRI Images

Wu, Jia; Xiao, Ping; Huang, Haojie; Gou, Fangfang; Zhou, Zhixun; Dai, Zhehao

doi:10.1109/jbhi.2022.3184930

Cited by 41 publications

(18 citation statements)

References 49 publications

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“…Osteosarcoma is the most common solid tumor of bone origin, accounting for approximately 20% of primary sarcomas of bone [ 1 ]. Osteosarcomas frequently occur in adolescents or children under 20 years of age, with more than 75% of patients having an age of onset younger than 25 years [ 2 ]. Although some osteosarcomas can be cured by surgical means, some cases remain that are highly fatal even with the most aggressive treatment measures.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Liu

et al. 2023

Diagnostics

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Bone malignant tumors are metastatic and aggressive. The manual screening of medical images is time-consuming and laborious, and computer technology is now being introduced to aid in diagnosis. Due to a large amount of noise and blurred lesion edges in osteosarcoma MRI images, high-precision segmentation methods require large computational resources and are difficult to use in developing countries with limited conditions. Therefore, this study proposes an artificial intelligence-aided diagnosis scheme by enhancing image edge features. First, a threshold screening filter (TSF) was used to pre-screen the MRI images to filter redundant data. Then, a fast NLM algorithm was introduced for denoising. Finally, a segmentation method with edge enhancement (TBNet) was designed to segment the pre-processed images by fusing Transformer based on the UNet network. TBNet is based on skip-free connected U-Net and includes a channel-edge cross-fusion transformer and a segmentation method with a combined loss function. This solution optimizes diagnostic efficiency and solves the segmentation problem of blurred edges, providing more help and reference for doctors to diagnose osteosarcoma. The results based on more than 4000 osteosarcoma MRI images show that our proposed method has a good segmentation effect and performance, with Dice Similarity Coefficient (DSC) reaching 0.949, and show that other evaluation indexes such as Intersection of Union (IOU) and recall are better than other methods.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Liu

et al. 2023

Diagnostics

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“…One study proposed a deep learning model based on reading pathological sections to improve the efficiency and accuracy of osteosarcoma diagnosis [ 12 ]. Some researchers have continuously improved the computer processing algorithms of magnetic resonance imaging (MRI) images while establishing computer-aided diagnostic processing schemes for osteosarcoma; after validating and analyzing more than 70,000 MRI images of osteosarcoma from Chinese hospitals, they proposed a system model based on MRI computer-aided processing (AIMSost) [ 13 , 14 , 15 , 16 ]. Artificial neural networks (ANNs) are mathematical models consisting of a large number of interconnected neurons that form diverse networks according to various connectivity methods, with biological nerves as the basis [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Construction of Osteosarcoma Diagnosis Model by Random Forest and Artificial Neural Network

Que

Yang

et al. 2023

JPM

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Osteosarcoma accounts for 28% of primary bone malignancies in adults and up to 56% in children and adolescents (<20 years). However, early diagnosis and treatment are still inadequate, and new improvements are still needed. Missed diagnoses exist due to fewer traditional diagnostic methods, and clinical symptoms are often already present before diagnosis. This study aimed to develop novel and efficient predictive models for the diagnosis of osteosarcoma and to identify potential targets for exploring osteosarcoma markers. First, osteosarcoma and normal tissue expression microarray datasets were downloaded from the Gene Expression Omnibus (GEO). Then we screened the differentially expressed genes (DEGs) in the osteosarcoma and normal groups in the training group. Next, in order to explore the biologically relevant role of DEGs, Metascape and enrichment analyses were also performed on DEGs. The “randomForest” and “neuralnet” packages in R software were used to select representative genes and construct diagnostic models for osteosarcoma. The next step is to validate the model of the artificial neural network. Then, we performed an immune infiltration analysis by using the training set data. Finally, we constructed a prognostic model using representative genes for prognostic analysis. The copy number of osteosarcoma was also analyzed. A random forest classifier identified nine representative genes (ANK1, TGFBR3, RSF21, HSPB8, ITGA7, RHD, AASS, GREM2, NFASC). HSPB8, RHD, AASS, and NFASC were genes we identified that have not been previously reported to be associated with osteosarcoma. The osteosarcoma diagnostic model we constructed has good performance with areas under the curves (AUCs) of 1 and 0.987 in the training and validation groups, respectively. This study opens new horizons for the early diagnosis of osteosarcoma and provides representative markers for the future treatment of osteosarcoma. This is the first study to pioneer the establishment of a genetic diagnosis model for osteosarcoma and advance the development of osteosarcoma diagnosis and treatment.

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“…Magnetic resonance imaging (MRI) can display the structure of soft tissue clearly and has higher contrast as well as resolution than other imaging methods [ 4 ], which makes the tumor area easier to distinguish. It is considered to be the best imaging method to evaluate the relationship between the primary osteosarcoma lesion and its surrounding areas [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

BA-GCA Net: Boundary-Aware Grid Contextual Attention Net in Osteosarcoma MRI Image Segmentation

Liu

Gou

et al. 2022

Computational Intelligence and Neuroscience

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Osteosarcoma is one of the most common bone tumors that occurs in adolescents. Doctors often use magnetic resonance imaging (MRI) through biosensors to diagnose and predict osteosarcoma. However, a number of osteosarcoma MRI images have the problem of the tumor shape boundary being vague, complex, or irregular, which causes doctors to encounter difficulties in diagnosis and also makes some deep learning methods lose segmentation details as well as fail to locate the region of the osteosarcoma. In this article, we propose a novel boundary-aware grid contextual attention net (BA-GCA Net) to solve the problem of insufficient accuracy in osteosarcoma MRI image segmentation. First, a novel grid contextual attention (GCA) is designed to better capture the texture details of the tumor area. Then the statistical texture learning block (STLB) and the spatial transformer block (STB) are integrated into the network to improve its ability to extract statistical texture features and locate tumor areas. Over 80,000 MRI images of osteosarcoma from the Second Xiangya Hospital are adopted as a dataset for training, testing, and ablation studies. Results show that our proposed method achieves higher segmentation accuracy than existing methods with only a slight increase in the number of parameters and computational complexity.

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An Artificial Intelligence Multiprocessing Scheme for the Diagnosis of Osteosarcoma MRI Images

Cited by 41 publications

References 49 publications

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Construction of Osteosarcoma Diagnosis Model by Random Forest and Artificial Neural Network

BA-GCA Net: Boundary-Aware Grid Contextual Attention Net in Osteosarcoma MRI Image Segmentation

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