Automated Bed Detection and Removal from Abdominal CT Images for Automatic Segmentation Applications

Rahni, Ashrani Aizzuddin Abd; Fuzaie, Muhamad Fazwan Mohamed; Irr, Omar Ibrahim Al

doi:10.1109/iecbes.2018.8626638

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…To assign specific tissue labels for each pixel, we first obtained a binary mask identifying the patient’s body by removing the background and CT bed using traditional image processing methods 41 . Specific tissue labels for fat and muscle within the body were automatically assigned using our segmentation CNN.…”

Section: Methodsmentioning

confidence: 99%

Opportunistic Assessment of Ischemic Heart Disease Risk Using Abdominopelvic Computed Tomography and Medical Record Data: a Multimodal Explainable Artificial Intelligence Approach

et al. 2021

Preprint

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Current risk scores for predicting ischemic heart disease (IHD) risk—the leading cause of global mortality—have limited efficacy. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8,197 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed improved multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient’s electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data can enhance IHD risk assessment and aid primary prevention efforts for IHD.

show abstract

Section: Methodsmentioning

confidence: 99%

Opportunistic Assessment of Ischemic Heart Disease Risk Using Abdominopelvic Computed Tomography and Medical Record Data: a Multimodal Explainable Artificial Intelligence Approach

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…To assign specific tissue labels for each pixel, we first obtained a binary mask identifying the patient's body by removing the background and CT bed using traditional image processing methods 42 . Specific tissue labels for adipose tissue and muscle within the body were automatically assigned using our segmentation CNN.…”

Section: Attribution Analysismentioning

confidence: 99%

Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach

Zambrano Chaves,

Wentland,

Desai

et al. 2023

Sci Rep

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Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events—the leading cause of global mortality—have known limitations and may be improved by imaging biomarkers. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8139 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient’s electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data, can enhance IHD risk assessment and aid primary prevention efforts for IHD. To further promote research, we release the Opportunistic L3 Ischemic heart disease (OL3I) dataset, the first public multimodal dataset for opportunistic CT prediction of IHD.

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Automatic bed removal from small animal CT images using deep learning based segmentation

Fisher,

Asher,

Oliver

et al. 2024

2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Confere

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Automated Bed Detection and Removal from Abdominal CT Images for Automatic Segmentation Applications

Cited by 4 publications

References 6 publications

Opportunistic Assessment of Ischemic Heart Disease Risk Using Abdominopelvic Computed Tomography and Medical Record Data: a Multimodal Explainable Artificial Intelligence Approach

Opportunistic Assessment of Ischemic Heart Disease Risk Using Abdominopelvic Computed Tomography and Medical Record Data: a Multimodal Explainable Artificial Intelligence Approach

Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach

Automatic bed removal from small animal CT images using deep learning based segmentation

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