Accurate Measurement of Cross-Sectional Area of Femoral Artery on MRI Sequences of Transcontinental Ultramarathon Runners Using Optimal Parameters Selection

Cheng, Da‐Chuan; Wu, Jhu-Fong; Kao, Yi‐Hsuan; Su, Ching‐Tzong; Liu, Shing‐Hong

doi:10.1007/s10916-016-0626-y

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Moreover, the difference between instance segmentation and semantic segmentation is that the former is able to differentiate between different objects of the same class. In computer vision, this is done by machine learning with some hand-crafted features, as shown in previous studies [22,23]. In this paper, we use the object detection method YOLO v4 to identify the locations of lesions and classify them into two classes: metastasis or not.…”

Section: Introductionmentioning

confidence: 99%

Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning

et al. 2021

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This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians’ final decisions.

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Section: Introductionmentioning

confidence: 99%

Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning

et al. 2021

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“…Experienced oncologists can delineate a lung tumor manually based on CT or positron emission computed tomography (PET) evidence, and some other clinical evidence. Image segmentation methods may be region-based [3], edge-based [4,5], or hybrid [6]. A semi-automated system allows the user to incorporate pre-defined constraints [7], or provide the interaction with the segmentation result.…”

Section: Introductionmentioning

confidence: 99%

Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks

Cheng

Chi

Yang

et al. 2020

Sensors

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In this study, we proposed a semi-automated and interactive scheme for organ contouring in radiotherapy planning for patients with non-small cell lung cancers. Several organs were contoured, including the lungs, airway, heart, spinal cord, body, and gross tumor volume (GTV). We proposed some schemes to automatically generate and vanish the seeds of the random walks (RW) algorithm. We considered 25 lung cancer patients, whose computed tomography (CT) images were obtained from the China Medical University Hospital (CMUH) in Taichung, Taiwan. The manual contours made by clinical oncologists were taken as the gold standard for comparison to evaluate the performance of our proposed method. The Dice coefficient between two contours of the same organ was computed to evaluate the similarity. The average Dice coefficients for the lungs, airway, heart, spinal cord, and body and GTV segmentation were 0.92, 0.84, 0.83, 0.73, 0.85 and 0.66, respectively. The computation time was between 2 to 4 min for a whole CT sequence segmentation. The results showed that our method has the potential to assist oncologists in the process of radiotherapy treatment in the CMUH, and hopefully in other hospitals as well, by saving a tremendous amount of time in contouring.

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“…Computer-aided technology such as computer-aided diagnosis (CAD) has been researched fruitfully. Accurate artery boundary detection was proposed to access the artery wall movement during a heart cycle [14,15] for the purpose of measuring artery compliance. A computer-aided detection system was proposed to access the mandibular cannel boundary on tooth X-ray panoramas to measure the maximum possible distance on dental implants for preventing nerve injury in the mandibular cannel [16].…”

Section: Introductionmentioning

confidence: 99%

Systematic Quantification of Cell Confluence in Human Normal Oral Fibroblasts

et al. 2020

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Background: The accurate determination of cell confluence is a critical step for generating reasonable results of designed experiments in cell biological studies. However, the cell confluence of the same culture may be diversely predicted by individual researchers. Herein, we designed a systematic quantification scheme implemented on the Matlab platform, the so-called “Confluence-Viewer” program, to assist cell biologists to better determine the cell confluence. Methods: Human normal oral fibroblasts (hOFs) seeded in 10 cm culture dishes were visualized under an inverted microscope for the acquisition of cell images. The images were subjected to the cell segmentation algorithm with top-hat transformation and the Otsu thresholding technique. A regression model was built using a quadratic model and shape-preserving piecewise cubic model. Results: The cell segmentation algorithm generated a regression curve that was highly correlated with the cell confluence determined by experienced researchers. However, the correlation was low when compared to the cell confluence determined by novice students. Interestingly, the cell confluence determined by experienced researchers became more diverse when they checked the same images without a time limitation (up to 1 min). Conclusion: This tool could prevent unnecessary human-made mistakes and meaningless repeats for novice researchers working on cell-based studies in health care or cancer research.

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Accurate Measurement of Cross-Sectional Area of Femoral Artery on MRI Sequences of Transcontinental Ultramarathon Runners Using Optimal Parameters Selection

Cited by 3 publications

References 19 publications

Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning

Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning

Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks

Systematic Quantification of Cell Confluence in Human Normal Oral Fibroblasts

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