Whole Body Bone Scintigram and Stage Classification of the Patients with Adenocarcinoma of Lung

Abstract Purpose We propose a deep learning-based image interpretation system for skeleton segmentation and extraction of hot spots of bone metastatic lesion from a whole-body bone scintigram followed by automated measurement of a bone scan index (BSI), which will be clinically useful. Methods The proposed system employs butterfly-type networks (BtrflyNets) for skeleton segmentation and extraction of hot spots of bone metastatic lesions, in which a pair of anterior and posterior images are processed simultaneously. BSI is then measured using the segmented bones and extracted hot spots. To further improve the networks, deep supervision (DSV) and residual learning technologies were introduced. Results We evaluated the performance of the proposed system using 246 bone scintigrams of prostate cancer in terms of accuracy of skeleton segmentation, hot spot extraction, and BSI measurement, as well as computational cost. In a threefold cross-validation experiment, the best performance was achieved by BtrflyNet with DSV for skeleton segmentation and BtrflyNet with residual blocks. The cross-correlation between the measured and true BSI was 0.9337, and the computational time for a case was 112.0 s. Conclusion We proposed a deep learning-based BSI measurement system for a whole-body bone scintigram and proved its effectiveness by threefold cross-validation study using 246 whole-body bone scintigrams. The automatically measured BSI and computational time for a case are deemed clinically acceptable and reliable.

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Extraction of metastasis hotspots in a whole-body bone scintigram based on bilateral asymmetry

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-021-02488-w ◽

2021 ◽

Author(s):

Atsushi Saito ◽

Hayato Wakabayashi ◽

Hiromitsu Daisaki ◽

Atsushi Yoshida ◽

Shigeaki Higashiyama ◽

...

Keyword(s):

Whole Body ◽

Bilateral Asymmetry ◽

Bone Scintigram ◽

Body Bone

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Age- and sex-related bone uptake of Tc-99m-HDP measured by whole-body bone scanning

Nuklearmedizin ◽

10.1055/s-0038-1632258 ◽

2000 ◽

Vol 39 (05) ◽

pp. 127-132 ◽

Cited By ~ 2

Author(s):

Nicole Sieweke ◽

K. H. Bohuslavizki ◽

W. U. Kampen ◽

M. Zuhayra ◽

M. Clausen ◽

...

Keyword(s):

Whole Body ◽

Bone Lesions ◽

Men And Women ◽

Bone Uptake ◽

Normal Bone ◽

Age Related ◽

Number Of Patients ◽

Wide Range ◽

Bone Scans ◽

Body Bone

Summary Aim of this study was to validate a recently introduced new and easy-to-perform method for quantifying bone uptake of Tc-99m-labelled diphosphonate in a routine clinical setting and to establish a normal data base for bone uptake depending on age and gender. Methods: In 49 women (14-79 years) and 47 men (6-89 years) with normal bone scans as well as in 49 women (33-81 years) and 37 men (27-88 years) with metastatic bone disease whole-body bone scans were acquired at 3 min and 3-4 hours p.i. to calculate bone uptake after correction for both urinary excretion and soft tissue retention. Results: Bone uptake values of various age-related subgroups showed no significant differences between men and women (p >0.05 ). Furthermore, no differences could be proven between age-matched subgroups of normals and patients with less than 10 metastatic bone lesions, while patients with wide-spread bone metastases revealed significantly increased uptake values. In both men and women highest bone uptake was obtained (p <0.05 ) in subjects younger than 20 years with active epiphyseal growth plates. In men, bone uptake slowly decreased with age up to 60 years and then showed a tendency towards increasing uptake values. In women, the mean uptake reached a minimun in the decade 20-29 years and then slowly increased with a positive linear correlation of age and uptake in subjects older than 55 years (r = 0.57). Conclusion: Since the results proposed in this study are in good agreement with data from literature, the new method used for quantification could be validated in a large number of patients. Furthermore, age- and sexrelated normal bone uptake values of Tc-99m-HDP covering a wide range of age could be presented for this method as a basis for further studies on bone uptake.

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Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

Applied Sciences ◽

10.3390/app10051797 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1797 ◽

Cited By ~ 2

Author(s):

Mera Kartika Delimayanti ◽

Bedy Purnama ◽

Ngoc Giang Nguyen ◽

Mohammad Reza Faisal ◽

Kunti Robiatul Mahmudah ◽

...

Keyword(s):

Machine Learning ◽

Sleep Stage ◽

Machine Learning Algorithms ◽

High Dimensional ◽

Sleep Stages ◽

Eeg Signals ◽

Stage Classification ◽

Sleep Stage Classification ◽

Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

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Evaluation of whole-body modalities for diagnosis of multifocal osteonecrosis—a pilot study

Arthritis Research & Therapy ◽

10.1186/s13075-021-02473-3 ◽

2021 ◽

Vol 23 (1) ◽

Author(s):

Shunichi Yokota ◽

Keita Sakamoto ◽

Yukie Shimizu ◽

Tsuyoshi Asano ◽

Daisuke Takahashi ◽

...

Keyword(s):

Study Data ◽

Whole Body ◽

Hematologic Disease ◽

Hip Joints ◽

Patient Demographics ◽

Examination Time ◽

History Of ◽

Body Magnetic Resonance Imaging ◽

The Cost ◽

Body Bone

Abstract Background This study aimed to investigate the ability of whole-body bone scintigraphy (WB-BS) in the detection of multifocal osteonecrosis (ON) compared to whole-body magnetic resonance imaging (WB-MRI) and to clarify the characteristics of patients with multifocal ON among those with ON of the femoral head (ONFH) using WB-MRI. Methods Forty-six patients who had symptomatic ONFH and underwent surgery in our hospital from April 2019 to October 2020 were included in the study. Data on patient demographics, including age, sex, body mass index (BMI), history of corticosteroid intake, alcohol abuse, smoking, and symptomatic joints, were collected from their medical records. All patients underwent WB-MRI and WB-BS before surgery. Results The agreement in the detection of ON by WB-MRI vs the uptake lesions by WB-BS in the hip joints was moderate (κ = 0.584), while that in other joints was low (κ < 0.40). Among the 152 joints with ON detected by WB-MRI, 92 joints (60.5%) were symptomatic, and 60 joints (39.5%) were asymptomatic. Twelve out of the 46 (26.0%) patients had multifocal (three or more distinct anatomical sites) ON. Nonetheless, while WB-BS detected symptomatic ON detected by WB-MRI as uptake lesions in 82.6% (76/92) of the joints, asymptomatic ON detected by WB-MRI was detected as uptake lesions in 21.7% (13/60) of the joints. All patients with multifocal ON had a history of steroid therapy, which was significantly higher than that in patients with oligofocal ON (P = 0.035). The patients with a hematologic disease had multifocal ON at a higher rate (P = 0.015). Conclusions It might be difficult for WB-BS to detect the asymptomatic ON detected by WB-MRI compared to symptomatic ON. Considering the cost, examination time, and radiation exposure, WB-MRI might be useful for evaluating multifocal ON. Larger longitudinal studies evaluating the benefits of WB-MRI for detecting the risk factors for multifocal ON are required.

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Cost-Benefit Analysis of Whole Body Bone Scintilography in the Pre-Trasnplant Assesment of Adult Patients Beares of Hepatocellular Carcinoma in a Liver Tranaplant List in the South of Brazil

Value in Health ◽

10.1016/j.jval.2015.09.213 ◽

2015 ◽

Vol 18 (7) ◽

pp. A812

Author(s):

G Balbinotto ◽

S Rodriguez ◽

A BrandÃo

Keyword(s):

Hepatocellular Carcinoma ◽

Cost Benefit Analysis ◽

Cost Benefit ◽

Adult Patients ◽

Whole Body ◽

Benefit Analysis ◽

The South ◽

South Of Brazil ◽

Body Bone

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A novel two-stage method of plant seedlings classification based on deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211507 ◽

2021 ◽

pp. 1-11

Author(s):

Tianhong Dai ◽

Shijie Cong ◽

Jianping Huang ◽

Yanwen Zhang ◽

Xinwang Huang ◽

...

Keyword(s):

Deep Learning ◽

Learning Technology ◽

Two Stage ◽

Second Stage ◽

Stage Classification ◽

Different Types ◽

Two Stages ◽

Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

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Using transfer learning for diabetic retinopathy stage classification

Applied Computing and Informatics ◽

10.1108/aci-07-2021-0191 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Enas M.F. El Houby

Keyword(s):

Diabetic Retinopathy ◽

Transfer Learning ◽

Grade Level ◽

Design Methodology ◽

Content Type ◽

Stage Classification ◽

Set Up ◽

Retinal Fundus ◽

Automated Methods

PurposeDiabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.Design/methodology/approachIn this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.FindingsBy conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.Originality/valueIn this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

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