scholarly journals Automated Segmentation of Kidney Cortex and Medulla in CT Images: A Multisite Evaluation Study

2021 ◽  
pp. ASN.2021030404
Author(s):  
Panagiotis Korfiatis ◽  
Aleksandar Denic ◽  
Marie Edwards ◽  
Adriana Gregory ◽  
Darryl Wright ◽  
...  
Keyword(s):  
Clinical Care ◽  
Evaluation Study ◽  
Ct Images ◽  
Kidney Cortex ◽  
Automated Segmentation ◽  
Reference Standard ◽  
Test Set ◽  
Contrast Enhanced Ct ◽  
Wide Range ◽  
Contrast Ct

Background: In kidney transplantation, a contrast CT scan is obtained in the candidate kidney donor to detect any subclinical pathology in the kidney. Recent work from the Aging Kidney Anatomy study has characterized kidney volumes, cortex volumes, and medulla volumes using a manual image processing tool. However, this technique is time consuming and impractical for clinical care, and thus, these measures are not obtained during donor evaluations. This study proposes a fully automated segmentation approach for measuring kidney, cortex, and medulla volumes. Methods: A total of 1930 contrast-enhanced CT exams with reference standard manual segmentations from one institution were used for development of the algorithm. A convolutional neural network model was trained (n=1238) and validated (n=306), and then evaluated in a hold-out test set of reference standard segmentations (n=386). After the initial evaluation, the algorithm was further tested on datasets originating from two external sites (n=1226). Results: The automated model was found to perform on par with manual segmentation with errors similar to interobserver variability with manual segmentations. Compared with the reference standard, the automated approach achieved a Dice similarity metric of 0.94 (right cortex), 0.90 (right medulla), 0.94 (left cortex), and 0.90 (left medulla) in the test set. Similar performance was observed when the algorithm was applied on the two external datasets. Conclusions: A fully automated approach for measuring cortex and medullary volumes in CT images of the kidneys has been established. This method may prove useful for a wide range of clinical applications.

A deep learning approach to generate contrast-enhanced computerised tomography angiograms without the use of intravenous contrast agents

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Chandrashekar ◽  
N Shivakumar ◽  
P Lapolla ◽  
A Handa ◽  
V Grau ◽  
...  
Keyword(s):  
Deep Learning ◽  
Soft Tissue ◽  
Contrast Agents ◽  
Ct Images ◽  
Learning Methods ◽  
Intravenous Contrast ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Contrast Ct

Abstract Introduction Contrast-enhanced computerised tomographic (CT) angiograms are widely used in cardiovascular imaging to obtain a non-invasive view of arterial structures. In aortic aneurysmal disease (AAA), CT angiograms are required prior to surgical intervention to differentiate between blood and the intra-luminal thrombus, which is present in 95% of cases. However, contrast agents are associated with complications at the injection site as well as renal toxicity leading to contrast-induced nephropathy (CIN) and renal failure. Purpose We hypothesised that the raw data acquired from a non-contrast CT contains sufficient information to differentiate blood and other soft tissue components. Therefore, we utilised deep learning methods to define the subtleties between the various components of soft tissue in order to simulate contrast enhanced CT images without the need of contrast agents. Methods Twenty-six AAA patients with paired non-contrast and contrast-enhanced CT images were randomly selected from an ethically approved ongoing study (Ethics Ref 13/SC/0250) and used for model training and evaluation (13/13). Non-contrast axial slices within the aneurysmal region from 10 patients (n=100) were sampled for the underlying Hounsfield unit (HU) distribution at the lumen, intra-luminal thrombus and interface locations, identified from their paired contrast axial slices. Subsequently, paired axial slices within the training cohort were augmented in a ratio of 10:1 to produce a total of 23,551 2-D images. We trained a 2-D Cycle Generative Adversarial Network (cycleGAN) for this non-contrast to contrast transformation task. Model output was assessed by comparison to the contrast image, which serves as a gold standard, using image similarity metrics (ex. SSIM Index). Results Sampling HUs within the non-contrast CT scan across multiple axial slices (Figure 1A) revealed significant differences between the blood flow lumen (yellow), blood/thrombus interface (red), and thrombus (blue) regions (p<0.001 for all comparisons). This highlighted the intrinsic differences between the regions and established the foundation for subsequent deep learning methods. The Non-Contrast-to-Contrast (NC2C)-cycleGAN was trained with a learning rate of 0.0002 for 200 epochs on 256 x 256 images centred around the aorta. Figure 1B depicts “contrast-enhanced” images generated from non-contrast CT images across the aortic length from the testing cohort. This preliminary model is able to differentiate between the lumen and intra-luminal thrombus of aneurysmal sections with reasonable resemblance to the ground truth. Conclusion This study describes, for the first time, the ability to differentiate between visually incoherent soft tissue regions in non-contrast CT images using deep learning methods. Ultimately, refinement of this methodology may negate the use of intravenous contrast and prevent related complications. CTA Generation from Non-Contrast CTs Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Clarendon

Interventions to change clinician behaviour in relation to suicide prevention care in the emergency department: A scoping review protocol

2020 ◽  
Author(s):  
Hwayeon Danielle Shin ◽  
Christine Cassidy ◽  
Janet Curran ◽  
Lori Weeks ◽  
Leslie Anne Campbell ◽  
...  
Keyword(s):  
Emergency Department ◽  
Suicide Prevention ◽  
Behaviour Change ◽  
Clinical Care ◽  
Grey Literature ◽  
Allied Health Professionals ◽  
Wide Range ◽  
Knowledge And Attitude ◽  
Clinician Behaviour ◽  
Google Search

Objective: This review aims to explore, characterize, and map the literature on interventions implemented to change emergency department (ED) clinicians’ behaviour related to suicide prevention using the Behaviour Change Wheel (BCW) as a guiding theoretical framework. Introduction: An ED is a critical place for suicide prevention. Yet, many patients who present with suicide-related thoughts and behaviours are discharged without proper assessment or appropriate treatment. Supporting clinicians (who provide direct clinical care, including nurses, physicians, allied health professionals) to make the desired behaviour change following evidence-based suicide prevention care is an essential step toward improving patient outcomes. However, reviews to date have yet to take a theoretical approach to investigate interventions implemented to change clinicians’ behaviour. Inclusion criteria: This review will consider literature that includes interventions that target ED clinicians’ behaviour change related to suicide prevention. Behaviour change refers to observable practice changes as well as proxy measures of behaviour change including knowledge and attitude. There are many ways in which an intervention can change clinicians’ behaviour (e.g., education, altering service delivery). This review will include a wide range of interventions that target behaviour change regardless of the type but exclude interventions that exclusively target patients.Methods: Multiple databases will be searched: PubMed, PsycInfo, CINAHL and Embase. We will also include grey literature, including Google search, ProQuest Dissertations and Theses Global, and Scopus conference papers. Full text of included studies will be reviewed, critically appraised and extracted. Extracted data will be coded to identify intervention functions using the BCW. Findings will be summarized in tables accompanied by narrative reports.

scholarly journals Multi-factorial considerations for intra-thoracic lymph node evaluations of healthy cats on computed tomographic images

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Ninlawan Thammasiri ◽  
Chutimon Thanaboonnipat ◽  
Nan Choisunirachon ◽  
Damri Darawiroj
Keyword(s):  
Lymph Node ◽  
Lymph Nodes ◽  
Ct Images ◽  
Slice Thickness ◽  
Maximum Width ◽  
Post Contrast ◽  
Computed Tomographic ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Abstract Background It is difficult to examine mild to moderate feline intra-thoracic lymphadenopathy via and thoracic radiography. Despite previous information from computed tomographic (CT) images of intra-thoracic lymph nodes, some factors from animals and CT setting were less elucidated. Therefore, this study aimed to investigate the effect of internal factors from animals and external factors from the CT procedure on the feasibility to detect the intra-thoracic lymph nodes. Twenty-four, client-owned, clinically healthy cats were categorized into three groups according to age. They underwent pre- and post-contrast enhanced CT for whole thorax followed by inter-group evaluation and comparison of sternal, cranial mediastinal, and tracheobronchial lymph nodes. Results Post contrast-enhanced CT appearances revealed that intra-thoracic lymph nodes of kittens were invisible, whereas the sternal, cranial mediastinal, and tracheobronchial nodes of cats aged over 7 months old were detected (6/24, 9/24 and 7/24, respectively). Maximum width of these lymph nodes were 3.93 ± 0.74 mm, 4.02 ± 0.65 mm, and 3.51 ± 0.62 mm, respectively. By age, lymph node sizes of these cats were not significantly different. Transverse lymph node width of males was larger than that of females (P = 0.0425). Besides, the detection score of lymph nodes was affected by slice thickness (P < 0.01) and lymph node width (P = 0.0049). Furthermore, an irregular, soft tissue structure, possibly the thymus, was detected in all juvenile cats and three mature cats. Conclusions Despite additional information on intra-thoracic lymph nodes in CT images, which can be used to investigate lymphatic-related abnormalities, age, sex, and slice thickness of CT images must be also considered.

scholarly journals Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong Zhu ◽  
Yingfan Mao ◽  
Jun Chen ◽  
Yudong Qiu ◽  
Yue Guan ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Ct Images ◽  
Textural Features ◽  
Mutation Status ◽  
Portal Venous Phase ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Venous Phase ◽  
Ct Texture Analysis

AbstractTo explore the value of contrast-enhanced CT texture analysis in predicting isocitrate dehydrogenase (IDH) mutation status of intrahepatic cholangiocarcinomas (ICCs). Institutional review board approved this study. Contrast-enhanced CT images of 138 ICC patients (21 with IDH mutation and 117 without IDH mutation) were retrospectively reviewed. Texture analysis was performed for each lesion and compared between ICCs with and without IDH mutation. All textural features in each phase and combinations of textural features (p < 0.05) by Mann–Whitney U tests were separately used to train multiple support vector machine (SVM) classifiers. The classification generalizability and performance were evaluated using a tenfold cross-validation scheme. Among plain, arterial phase (AP), portal venous phase (VP), equilibrium phase (EP) and Sig classifiers, VP classifier showed the highest accuracy of 0.863 (sensitivity, 0.727; specificity, 0.885), with a mean area under the receiver operating characteristic curve of 0.813 in predicting IDH mutation in validation cohort. Texture features of CT images in portal venous phase could predict IDH mutation status of ICCs with SVM classifier preoperatively.

scholarly journals Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model

2021 ◽  
Author(s):  
Yunchao Yin ◽  
Derya Yakar ◽  
Rudi A. J. O. Dierckx ◽  
Kim B. Mouridsen ◽  
Thomas C. Kwee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Fibrosis ◽  
Liver Parenchyma ◽  
Ct Images ◽  
Fibrosis Stage ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Liver Surface ◽  
Fibrosis Staging

Abstract Objectives Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning. Methods The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage. Results The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2–F4), advanced fibrosis (F3–F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4). Conclusions Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning–based liver fibrosis staging algorithms. Key Points • Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency. • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network. • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage.

scholarly journals 322. Evaluation of the BioFire® Bone and Joint Infection (BJI) Panel for the Detection of Microorganisms and Antimicrobial Resistance Genes in Synovial Fluid Specimens

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S233-S234
Author(s):  
Corrin Graue ◽  
Bryan H Schmitt ◽  
Amy Waggoner ◽  
Frederic Laurent ◽  
Lelia Abad ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Synovial Fluid ◽  
Resistance Genes ◽  
Clinical Care ◽  
Joint Infection ◽  
High Morbidity ◽  
Bone And Joint Infection ◽  
Microbiological Methods ◽  
Antimicrobial Resistance Genes ◽  
Wide Range

Abstract Background Bone and Joint Infections (BJIs) present with non-specific symptoms that may include pain, swelling, and fever and are associated with high morbidity and significant risk of mortality. BJIs can be caused by a variety of bacteria and fungi, including anaerobes and microorganisms that can be challenging to culture or identify by traditional microbiological methods. Clinicians primarily rely on culture to identify the pathogen(s) responsible for infection. The BioFire® Bone and Joint Infection (BJI) Panel (BioFire Diagnostics, Salt Lake City, UT) is designed to detect 15 gram-positive bacteria (including seven anaerobes), 14 gram-negative bacteria (including one anaerobe), two yeast, and eight antimicrobial resistance (AMR) genes from synovial fluid specimens in about an hour. The objective of this study was to evaluate the performance of an Investigational Use Only (IUO) version of the BioFire BJI Panel compared to various reference methods. Methods Remnant synovial fluid specimens, which were collected for routine clinical care at 13 study sites in the US and Europe, underwent testing using an IUO version of the BioFire BJI Panel. Performance of this test was determined by comparison to Standard of Care (SoC) consisting of bacterial culture performed at each study site according to their routine procedures. Results A total of 1544 synovial fluid specimens were collected and tested with the BioFire BJI Panel. The majority of specimens were from knee joints (77.9%) and arthrocentesis (79.4%) was the most common collection method. Compared to SoC culture, overall sensitivity was 90.2% and specificity was 99.8%. The BioFire BJI Panel yielded a total of 268 Detected results, whereas SoC yielded a total of 215 positive results for on-panel analytes. Conclusion The BioFire BJI Panel is a sensitive, specific, and robust test for rapid detection of a wide range of analytes in synovial fluid specimens. The number of microorganisms and resistance genes included in the BioFire BJI Panel, together with a reduced time-to-result and increased diagnostic yield compared to culture, is expected to aid in the timely diagnosis and appropriate management of BJIs. Disclosures Benjamin von Bredow, PhD, BioFire (Grant/Research Support) Jennifer Dien Bard, PhD, BioFire Diagnostic (Consultant, Scientific Research Study Investigator) Bart Kensinger, PhD, BioFire Diagnostics (Employee) Benedicte Pons, PhD, bioMerieux SA (Employee) Corinne Jay, PhD, bioMerieux SA (Employee)

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