Bayesian Feature Pyramid Networks for Automatic Multi-Label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic Ratio

Solovyev, Roman; Melekhov, Iaroslav; Lesonen, Timo; Vaattovaara, Elias; Tervonen, Osmo; Tiulpin, Aleksei

doi:10.48550/arxiv.1908.02924

Cited by 2 publications

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“…We conducted experiments on the publicly available JSRT dataset and compared the results with three other different methods in currently published literature ( Supplemental Table S1 ) [ 26 , 27 , 28 ]. Our model ranked second in average Dice coefficient after Eslami et al’s work with the pix2pix model.…”

Section: Discussionmentioning

confidence: 99%

Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients

et al. 2023

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Cardiomegaly is associated with poor clinical outcomes and is assessed by routine monitoring of the cardiothoracic ratio (CTR) from chest X-rays (CXRs). Judgment of the margins of the heart and lungs is subjective and may vary between different operators. Methods: Patients aged > 19 years in our hemodialysis unit from March 2021 to October 2021 were enrolled. The borders of the lungs and heart on CXRs were labeled by two nephrologists as the ground truth (nephrologist-defined mask). We implemented AlbuNet-34, a U-Net variant, to predict the heart and lung margins from CXR images and to automatically calculate the CTRs. Results: The coefficient of determination (R2) obtained using the neural network model was 0.96, compared with an R2 of 0.90 obtained by nurse practitioners. The mean difference between the CTRs calculated by the nurse practitioners and senior nephrologists was 1.52 ± 1.46%, and that between the neural network model and the nephrologists was 0.83 ± 0.87% (p < 0.001). The mean CTR calculation duration was 85 s using the manual method and less than 2 s using the automated method (p < 0.001). Conclusions: Our study confirmed the validity of automated CTR calculations. By achieving high accuracy and saving time, our model can be implemented in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients

et al. 2023

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“…For instance, [9,[12][13][14] provided in depth details about the deep learning architectures, their strengths, and challenges in general. A good deal of literature, including [15][16][17][18][19], discuss stated architectures for medical image analysis. The focus of these efforts is around classification and prediction at image level [14].…”

Section: Introductionmentioning

confidence: 99%

“…For localization with bounding box and segmentation, Refs. [6,7,[15][16][17][18][19][20][21] have provided brief details for X-ray images. For instance, in survey [6], several articles regarding the application of deep learning on chest radiographs were examined that were published prior to March 2021.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Tools and Techniques for Localizing Abnormalities in X-ray Images Using Deep Learning

et al. 2022

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Deep learning is expanding and continues to evolve its capabilities toward more accuracy, speed, and cost-effectiveness. The core ingredients for getting its promising results are appropriate data, sufficient computational resources, and best use of a particular algorithm. The application of these algorithms in medical image analysis tasks has achieved outstanding results compared to classical machine learning approaches. Localizing the area-of-interest is a challenging task that has vital importance in computer aided diagnosis. Generally, radiologists interpret the radiographs based on their knowledge and experience. However, sometimes, they can overlook or misinterpret the findings due to various reasons, e.g., workload or judgmental error. This leads to the need for specialized AI tools that assist radiologists in highlighting abnormalities if exist. To develop a deep learning driven localizer, certain alternatives are available within architectures, datasets, performance metrics, and approaches. Informed decision for selection within the given alternative can lead to batter outcome within lesser resources. This paper lists the required components along-with explainable AI for developing an abnormality localizer for X-ray images in detail. Moreover, strong-supervised vs weak-supervised approaches have been majorly discussed in the light of limited annotated data availability. Likewise, other correlated challenges have been presented along-with recommendations based on a relevant literature review and similar studies. This review is helpful in streamlining the development of an AI based localizer for X-ray images while extendable for other radiological reports.

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Bayesian Feature Pyramid Networks for Automatic Multi-Label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic Ratio

Cited by 2 publications

References 0 publications

Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients

Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients

A Survey on Tools and Techniques for Localizing Abnormalities in X-ray Images Using Deep Learning

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