The Development of an Automatic Rib Sequence Labeling System on Axial Computed Tomography Images with 3-Dimensional Region Growing

Seol, Yu Jin; Kim, Young Jae; Park, Young‐Taek; Lee, Hee Young; Kim, Kwang Gi

doi:10.3390/s22124530

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…Terefore, extracting features from all the images leads to inaccurate diagnostic results. Terefore, it is necessary to separate the regions with interest from other regions by segmentation algorithms [36]. Te segmentation algorithms separate the afected pixels from health.…”

Section: Adaptive Region Growingmentioning

confidence: 99%

Analysis of Histopathological Images for Early Diagnosis of Oral Squamous Cell Carcinoma by Hybrid Systems Based on CNN Fusion Features

Ahmed

Senan

Shatnawi

2023

International Journal of Intelligent Systems

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Oral squamous cell carcinoma (OSCC) is one of the deadliest and most common types of cancer. The incidence of OSCC is increasing annually, which requires early diagnosis to receive appropriate treatment. The biopsy technique is one of the most important techniques for analyzing samples, but it takes a long time to get results. Manual diagnosis is still subject to errors and differences in doctors’ opinions, especially in the early stages. Thus, automated techniques can help doctors and patients to receive appropriate treatment. This study developed several hybrid models based on the fused CNN features for diagnosing OSCC-100x and OSCC-400x datasets for oral cancer, which have the ability to analyze medical images with a high level of precision and accuracy. They can detect subtle patterns, abnormalities, or indicators of diseases that may be difficult to recognize with the naked eye. The systems have the potential to significantly reduce human error and provide more consistent and reliable results, resulting in improved diagnostic accuracy. The systems also have the potential for early detection of OSCC for treatment success and improved patient outcomes. By detecting diseases at an early stage, clinicians can initiate interventions in a timely manner, potentially preventing OSCC progression and improving the chances of successful treatment. The first strategy was based on GoogLeNet, ResNet101, and VGG16 models pretrained, which did not achieve satisfactory results. The second strategy was based on GoogLeNet, ResNet101, and VGG16 models based on the adaptive region growing (ARG) segmentation algorithm. The third strategy is based on a mixed technique between GoogLeNet, ResNet101, and VGG16 models and ANN and XGBoost networks based on the ARG hashing algorithm. The fourth strategy for oral cancer diagnosis by ANN and XGBoost is based on features fused between CNN models. The ANN with fusion features of GoogLeNet-ResNet101-VGG16 yielded an AUC of 98.85%, accuracy of 99.3%, sensitivity of 98.2%, precision of 99.5%, and specificity of 98.35%.

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Section: Adaptive Region Growingmentioning

confidence: 99%

Analysis of Histopathological Images for Early Diagnosis of Oral Squamous Cell Carcinoma by Hybrid Systems Based on CNN Fusion Features

Ahmed

Senan

Shatnawi

2023

International Journal of Intelligent Systems

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“…Imaging systems using X-rays are essential in the field of diagnostic medical imaging [ 1 , 2 ]. This imaging system uses a detector to acquire information about how X-rays penetrate and attenuate materials (information for linear attenuation coefficient) as a final image [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

No-Reference-Based and Noise Level Evaluations of Cinematic Rendering in Bone Computed Tomography

Shim,

Lee

2024

Bioengineering

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Cinematic rendering (CR) is a new 3D post-processing technology widely used to produce bone computed tomography (CT) images. This study aimed to evaluate the performance quality of CR in bone CT images using blind quality and noise level evaluations. Bone CT images of the face, shoulder, lumbar spine, and wrist were acquired. Volume rendering (VR), which is widely used in the field of diagnostic medical imaging, was additionally set along with CR. A no-reference-based blind/referenceless image spatial quality evaluator (BRISQUE) and coefficient of variation (COV) were used to evaluate the overall quality of the acquired images. The average BRISQUE values derived from the four areas were 39.87 and 46.44 in CR and VR, respectively. The difference between the two values was approximately 1.16, and the difference between the resulting values increased, particularly in the bone CT image, where metal artifacts were observed. In addition, we confirmed that the COV value improved by 2.20 times on average when using CR compared to VR. This study proved that CR is useful in reconstructing bone CT 3D images and that various applications in the diagnostic medical field will be possible.

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Automatic rib segmentation and sequential labeling via multi-axial slicing and 3D reconstruction

Kim,

Ko,

Bum

et al. 2024

Appl Intell

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The Development of an Automatic Rib Sequence Labeling System on Axial Computed Tomography Images with 3-Dimensional Region Growing

Cited by 4 publications

References 40 publications

Analysis of Histopathological Images for Early Diagnosis of Oral Squamous Cell Carcinoma by Hybrid Systems Based on CNN Fusion Features

Analysis of Histopathological Images for Early Diagnosis of Oral Squamous Cell Carcinoma by Hybrid Systems Based on CNN Fusion Features

No-Reference-Based and Noise Level Evaluations of Cinematic Rendering in Bone Computed Tomography

Automatic rib segmentation and sequential labeling via multi-axial slicing and 3D reconstruction

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