A deep learning-based precision volume calculation approach for kidney and tumor segmentation on computed tomography images

Hsiao, Chiu-Han; Sun, Tzu-Lung; Lin, Ping-Cherng; Peng, Tsung-Yu; Chen, Yu‐Hsin; Cheng, Chieh-Yun; Yang, Feng-Jung; Yang, Shan-Yi; Wu, Cho‐Kai; Lin, Frank Yeong-Sung; Huang, Yennun

doi:10.1016/j.cmpb.2022.106861

Cited by 13 publications

(9 citation statements)

References 19 publications

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“…The first step in the process of reducing the workload and costs was kidneys volumetric analysis, which, for instance, can now be completely performed by DL algorithms both on ultrasound 138 , 139 and CT scans. 140 , 141 This can improve the accuracy of images pre-processing for subsequent radiomics features extraction. 141 Moreover, accurate volumetric segmentation of kidneys and tumor is pivotal when nephron sparing surgery is planned in patients with kidney cancer.…”

Section: Discussionmentioning

confidence: 99%

“…140,141 This can improve the accuracy of images pre-processing for subsequent radiomics features extraction. 141 Moreover, accurate volumetric segmentation of kidneys and tumor is pivotal when nephron sparing surgery is planned in patients with kidney cancer. 142 Models trained and validated indicate a roughly big difference in AUCs obtained from these studies (from 0.64 to 0.97).…”

Section: Discussionmentioning

confidence: 99%

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Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

Russo

Crocetto

Barone

et al. 2023

Therapeutic Advances in Urology

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Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

Russo

Crocetto

Barone

et al. 2023

Therapeutic Advances in Urology

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“…Moreover, they have the potential to enhance both labor and time efficiency. Ultimately, these strategies yield reliable and exceedingly precise identification outcomes [31] .…”

Section: Kidney Cancer Segmentation Studiesmentioning

confidence: 99%

“…The technique yielded a recall rate of 96.13% and a Jacquard score of 95.4%. Another study by [31], proposed pre-processing and verification strategies for each CT scan. The ResNet model is utilized in the pre-processing stage to train processed training data to analyze performance indicators.…”

Section: Kidney Cancer Segmentation Studiesmentioning

confidence: 99%

A Comprehensive Review of Artificial Intelligence Approaches in Kidney Cancer Medical Images Diagnosis, Datasets, Challenges and Issues and Future Directions

Abdalredha Kadhim,

Abed Mohammed

2024

ijmscs

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Various diseases are prevalent in global populations and can be attributed to human lifestyles, economic conditions, social factors, genetics, and other country-specific factors. Our study provides a detailed analysis of the current methods and uses of artificial intelligence (AI) in diagnosing kidney cancer. This literature review examines the most recent advancements and uses of AI in diagnosing cancer of the kidneys, emphasizing its significant influence on medical diagnosis and patient treatment. Evaluate their efficacy, explore prospective areas for future study and development, and identify obstacles and constraints, offering a fundamental review of critical advancements in AI, machine learning, and deep learning. AI-powered diagnostic tools expedite the analysis of intricate images and enhance the early identification of diseases, ultimately leading to improved patient results. Furthermore, the utilization of AI-driven image processing enables the customization of treatment regimens, thereby enhancing the efficiency of healthcare provision. This review primarily highlights the importance of conducting additional research and improving AI to transform the detection and treatment of kidney cancer.

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“…In a work of 2022, Hsiao et al [41] used a Unet with a ResNet-41 as encoder to calculate the total kidney volume (TKV), i. e., the sum of the size of both kidneys including tumors and cysts. They trained their networks on 210 publicly available cases from the Kidney and Kidney Tumor Segmentation Challenge (KiTS) dataset [42] using 5-fold cross-validation.…”

Section: Kidney Volumetrymentioning

confidence: 99%

Artificial Intelligence-powered automatic volume calculation in medical images – available tools, performance and challenges for nuclear medicine

Wendler,

Kreissl,

Schemmer

et al. 2023

Nuklearmedizin

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Volumetry is crucial in oncology and endocrinology, for diagnosis, treatment planning, and evaluating response to therapy for several diseases. The integration of Artificial Intelligence (AI) and Deep Learning (DL) has significantly accelerated the automatization of volumetric calculations, enhancing accuracy and reducing variability and labor. In this review, we show that a high correlation has been observed between Machine Learning (ML) methods and expert assessments in tumor volumetry; Yet, it is recognized as more challenging than organ volumetry. Liver volumetry has shown progression in accuracy with a decrease in error. If a relative error below 10 % is acceptable, ML-based liver volumetry can be considered reliable for standardized imaging protocols if used in patients without major anomalies. Similarly, ML-supported automatic kidney volumetry has also shown consistency and reliability in volumetric calculations. In contrast, AI-supported thyroid volumetry has not been extensively developed, despite initial works in 3D ultrasound showing promising results in terms of accuracy and reproducibility. Despite the advancements presented in the reviewed literature, the lack of standardization limits the generalizability of ML methods across diverse scenarios. The domain gap, i. e., the difference in probability distribution of training and inference data, is of paramount importance before clinical deployment of AI, to maintain accuracy and reliability in patient care. The increasing availability of improved segmentation tools is expected to further incorporate AI methods into routine workflows where volumetry will play a more prominent role in radionuclide therapy planning and quantitative follow-up of disease evolution.

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A deep learning-based precision volume calculation approach for kidney and tumor segmentation on computed tomography images

Cited by 13 publications

References 19 publications

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

A Comprehensive Review of Artificial Intelligence Approaches in Kidney Cancer Medical Images Diagnosis, Datasets, Challenges and Issues and Future Directions

Artificial Intelligence-powered automatic volume calculation in medical images – available tools, performance and challenges for nuclear medicine

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