Erik Smistad

SINTEF | Stiftelsen for industriell og teknisk forskning · Department of Medical technology

Segmentation of anatomical structures, from modalities like computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound, is a key enabling technology for medical applications such as diagnostics, planning and guidance. More efficient implementations are necessary, as most segmentation methods are computationally expensive, and the amo...

Deep convolutional neural networks have quickly become the standard for medical image analysis. Although there are many frameworks focusing on training neural networks, there are few that focus on high performance inference and visualization of medical images. Neural network inference requires an inference engine (IE), and there are currently sever...

Volume and ejection fraction (EF) measurements of the left ventricle (LV) in 2D echocardiography is associated with a high uncertainty due to inter-observer variability of the manual measurement, but also due to ultrasound acquisition errors such as apical foreshortening. In this work, a real-time and fully automated ejection fraction measurement a...

Ultrasound images acquired during axillary nerve block procedures can be difficult to interpret. Highlighting the important structures, such as nerves and blood vessels, may be useful for the training of inexperienced users. A deep convolutional neural network is used to identify the musculocutaneous, median, ulnar, and radial nerves, as well as th...

Background Accurate quantification of left ventricular (LV) systolic function is fundamental in echocardiography. LV Global Longitudinal Strain (GLS) offers advantages over LV ejection fraction, being more sensitive, reproducible and offering better prognostic value. However, existing semi-automatic methods are time-consuming and heavily operator-d...

Background Accurate quantification of left ventricular (LV) wall thickness and chamber dimensions in the echocardiographic parasternal long-axis view (PLAX) is crucial for clinical decisions in patients with heart disease. However, there is a need for more reproducible and time-efficient methods. Fully automated deep learning (DL)-based methods cou...

Aims The clinical utility of regional strain measurements in echocardiography is challenged by suboptimal reproducibility. In this study, we aimed to evaluate the test–retest reproducibility of regional longitudinal strain (RLS) per coronary artery perfusion territory (RLSTerritory) and basal-to-apical level of the left ventricle (RLSLevel), measur...

Automatic estimation of cardiac ultrasound image quality can be beneficial for guiding operators and ensuring the accuracy of clinical measurements. Previous work often fails to distinguish the view correctness of the echocardiogram from the image quality. Additionally, previous studies only provide a global image quality value, which limits their...

Considering the increased workload in pathology laboratories today, automated tools such as artificial intelligence models can help pathologists with their tasks and ease the workload. In this paper, we are proposing a segmentation model (DRU-Net) that can provide a delineation of human non-small cell lung carcinomas and an augmentation method that...

Fully automatic cardiac segmentation can be a fast and reproducible method to extract clinical measurements from an echocardiography examination. The U-Net architecture is the current state-of-the-art deep learning architecture for medical segmentation and can segment cardiac structures in real-time with average errors comparable to inter-observer...

Aims Impaired standardization of echocardiograms may increase inter-operator variability. This study aimed to determine whether the real-time guidance of experienced sonographers by deep learning (DL) could improve the standardization of apical recordings. Methods and results Patients (n = 88) in sinus rhythm referred for echocardiography were inc...

Background: Digital pathology enables automatic analysis of histopathological sections using artificial intelligence (AI). Automatic evaluation could improve diagnostic efficiency and help find associations between morphological features and clinical outcome. For development of such prediction models, identifying invasive epithelial cells, and sepa...

Background and aims: Echocardiography is a cornerstone in cardiac imaging and left ventricular (LV) ejection fraction (EF) is a key parameter for patient management. Recent advances in artificial intelligence (AI) have enabled fully automatic measurements of LV volumes and EF both during scanning and in stored recordings. The aim of this study was...

Aims Apical foreshortening leads to an underestimation of left ventricular (LV) volumes and an overestimation of LV ejection fraction and global longitudinal strain. Real-time guiding using deep learning (DL) during echocardiography to reduce foreshortening could improve standardization and reduce variability. We aimed to study the effect of real-t...

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Norwegian University of Science and Technology, St. Olavs University Hospital, Central-Norway Health Authority. Background Left ventricular (LV) ejection fraction (EF) and left ventricular volumes are key parameters for characterisatio...

Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Norwegian University of Science and Technology. Background Left ventricular (LV) volumes and ejection fraction (EF) are the most used and studied parameters in echocardiography, but they are hampered with the tedious nature and limited reproducibility...

Aims: Assessment of left ventricular (LV) function by echocardiography is hampered by modest test-retest reproducibility. A novel artificial intelligence (AI) method based on deep learning provides fully automated measurements of LV global longitudinal strain (GLS) and may improve the clinical utility of echocardiography by reducing user related v...

Introduction Optic nerve sheath diameter (ONSD) has shown promise as a noninvasive parameter for estimating intracranial pressure (ICP). In this study, we evaluated a novel automated method of measuring the ONSD in transorbital ultrasound imaging. Methods From adult traumatic brain injury (TBI) patients with invasive ICP monitoring, bedside manual...

Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2-D ultrasound imaging. The reliability of these measurements depends on the correct pose of the transducer such that the 2-D imaging plane properly aligns with the heart for standard measurement views and is thus dependent on th...

Background Global longitudinal strain (GLS) is recommended for assessment of left ventricular (LV) function. Test-retest variability of GLS rely on recordings and analyses. Foreshortened LV recordings are shown to reduce length measurements and increase GLS. Real-time guiding of operators and automated GLS analyses (auto-GLS) may improve echocardio...

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer...

p>Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2D ultrasound imaging. The reliability of these measurements strongly depends on the correct pose of the transducer such that the 2D imaging plane properly aligns with the heart for standard measurement views, and is thus depen...

p>Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2D ultrasound imaging. The reliability of these measurements strongly depends on the correct pose of the transducer such that the 2D imaging plane properly aligns with the heart for standard measurement views, and is thus depen...

Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): PIC from European Union"s Horizon 2020 Marie Skłodowska-Curie Actions ITN Background The wall thickness of the left ventricle (LV) is an important parameter in the diagnosis of hypertension and more specifically in hypertrophic cardiomyopathy. A...

Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Norwegian University of Science and Technology, St. Olavs University Hospital, Central-Norway Health Authority Background Left ventricular (LV) foreshortening is common in echocardiography and may impair reproducibility and estimation of LV function. Fe...

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Norwegian University of Science and Technology, St. Olavs University Hospital, Central-Norway Health Authority OnBehalf Department of Circulation and Medical imaging, Norwegian University of Science and Technology, Trondheim, Norway Ba...

Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the ability to write computer code or purchase commercial solutions. We present a code-free pipeline utilizing free-to-use, open-source software (QuPath, DeepMIB, and FastPathol...

The annotated colon biopsies is made openly available for any purpose on DataverseNO (https://doi.org/10.18710/TLA01U). The shared README.txt file contains description of the dataset and its content. The actual data and info about its origin can be found through the URL.

This document contains the table with adjusted p-values from the pairwise Tukey's range tests referenced in section 3.3 (see Table S1). Red colour indicate no significance (p-value ≥ 0.05), green colour indicate slight significance (p-value ∈ [0.001, 0.05], and light green colour indicate strong significance (p-value < 0.001). Pairwise comparisons...

Histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohor...

Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the ability to write computer code or purchase commercial solutions. We present a code-free pipeline utilizing free-to-use, open-source software (QuPath, DeepMIB, and FastPathol...

Most work on left ventricle (LV) ultrasound image segmentation using deep learning has focused on single-frame segmentation of end-diastole (ED) and end-systole (ES) frames. Using these neural network models on the entire cardiac cycle often results in segmentation flickering and sudden large segmentation errors. Neural networks that perform some f...

Objectives This study sought to examine if fully automated measurements of global longitudinal strain (GLS) using a novel motion estimation technology based on deep learning and artificial intelligence (AI) are feasible and comparable with a conventional speckle-tracking application. Background GLS is an important parameter when evaluating left ve...

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of...

Deformation imaging in echocardiography has been shown to have better diagnostic and prognostic value than conventional anatomical measures such as ejection fraction. However, despite clinical availability and demonstrated efficacy, everyday clinical use remains limited at many hospitals. The reasons are complex, but practical robustness has been q...

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of...

Long Abstract of preliminary work of FastPathology submitted to IPCAI 2020. Video presentation is openly available on YouTube: https://www.youtube.com/watch?v=1s7jU6T7S3U Abstract can also be found on the conference website: https://ilkerhac.wixsite.com/ipcai2020/virtual-ipcai

Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semi-automatically in clinical routine, and is thus prone to inter and intra observer variability. Recent studies have shown that deep learning has the potential to perform fully automatic segmentation. Howeve...

Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semi-automatically in clinical routine, and is thus prone to inter- and intra-observer variability. Recent studies have shown that deep learning has the potential to perform fully automatic segmentation. Howev...

Funding Acknowledgements The Norwegian Health Association, South-Eastern Norway regional health Authority and the national program for clinical therapy research (KLINBEFORSK). Background Global longitudinal strain (GLS) by echocardiography has incremental prognostic value in patients with acute myocardial infarction and heart failure compared to l...

It has been shown that deep neural networks can accuratly segment the left ventricle (LV), myocardium and left atrium in apical two and four chamber (A2C and A4C) views. While segmentation of apical long-axis (ALAX) views is quite similar to A2C and A4C, there is one major difference; the left ventricular outflow tract (LVOT) which restricts the my...

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing...

We recently published a deep learning study on the potential of encoder-decoder networks for the segmentation of the 2D CAMUS ultrasound dataset. We propose in this abstract an extension of the evaluation criteria to anatomical assessment, as traditional geometric and clinical metrics in cardiac segmentation do not take into account the anatomical...

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing...

Transthoracic echocardiography examinations are usually performed according to a protocol comprising different probe postures providing standard views of the heart. These are used as a basis when assessing cardiac function, and it is essential that the morphophysiological representations are correct. Clinical analysis is often initialized with the...

Recent studies in the field of deep learning suggest that motion estimation can be treated as a learnable problem. In this paper we propose a pipeline for functional imaging in echocardiography consisting of four central components, (i) classification of cardiac view, (ii) semantic partitioning of the left ventricle (LV) myocardium, (iii) regional...

Deep convolutional neural networks have achieved great results on image classification problems. In this paper, a new method using a deep convolutional neural network for detecting blood vessels in B-mode ultrasound images is presented. Automatic blood vessel detection may be useful in medical applications such as deep venous thrombosis detection,...

Ultrasound-guided regional anesthesia can be challenging, especially for inexperienced physicians. The goal of the proposed methods is to create a system that can assist a user in performing ultrasound-guided femoral nerve blocks. The system indicates in which direction the user should move the ultrasound probe to investigate the region of interest...

The measurement of blood flow velocities using either conventional color flow imaging (CFI), or more recent vector-Doppler or blood speckle tracking (BST) approaches are all hampered by regions of signal dropouts due to clutter filtering, as well as potentially high variance depending on the SNR in a given scenario. The aim of this work is to descr...

Introduction: Our motivation is increased bronchoscopic diagnostic yield and optimized preparation, for navigated bronchoscopy. In navigated bronchoscopy, virtual 3D airway visualization is often used to guide a bronchoscopic tool to peripheral lesions, synchronized with the real time video bronchoscopy. Visualization during navigated bronchoscopy...

Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been propo...