Filters








12 Hits in 8.7 sec

MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images [article]

Andres Diaz-Pinto, Sachidanand Alle, Alvin Ihsani, Muhammad Asad, Vishwesh Nath, Fernando Pérez-García, Pritesh Mehta, Wenqi Li, Holger R. Roth, Tom Vercauteren, Daguang Xu, Prerna Dogra (+3 others)
2022 arXiv   pre-print
To address this problem, we present MONAI Label, a free and open-source platform that facilitates the development of AI-based applications that aim at reducing the time required to annotate 3D medical  ...  The lack of annotated datasets is a major challenge in training new task-specific supervised AI algorithms as manual annotation is expensive and time-consuming.  ...  training of deep learning algorithm for 3D medical image segmentation.  ... 
arXiv:2203.12362v1 fatcat:imdyysslozdp3meoi3f7njhyiq

Pulmonary Nodule Classification in Lung Cancer from 3D Thoracic CT Scans Using fastai and MONAI

Satheshkumar Kaliyugarasan, Arvid Lundervold, Alexander Selvikvåg Lundervold
2021 International Journal of Interactive Multimedia and Artificial Intelligence  
To construct and train our model, we use our novel extension of the fastai deep learning framework to 3D medical imaging tasks, combined with the MONAI deep learning library.  ...  Class activation maps are applied to investigate the features used by our classifier, enabling a rudimentary level of explainability for what is otherwise close to "black box" predictions.  ...  This approach to medical image analysis can lead to valuable insights and assistance in imaging diagnostics.  ... 
doi:10.9781/ijimai.2021.05.002 fatcat:3slbe35ttvar3jpqe3qg5cd24y

A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans [article]

Shiyi Wang, Guang Yang
2022 arXiv   pre-print
For future medical use, embedding the model into the medical facilities might be an efficient way of assisting or substituting doctors with diagnoses, therefore, a broader range of the problem of variant  ...  Medical image classification and segmentation based on deep learning (DL) are emergency research topics for diagnosing variant viruses of the current COVID-19 situation.  ...  MONAI Framework, an open-source AI framework from NVIDIA and King's College London in late 2019, is a free, community-supported, PyTorch-based framework for deep learning in medical imaging [3] .  ... 
arXiv:2208.02910v1 fatcat:5dbe62ia5vgivon5r7na5dcnre

Teacher-student approach for lung tumor segmentation from mixed-supervised datasets

Vemund Fredriksen, Svein Ole M. Sevle, André Pedersen, Thomas Langø, Gabriel Kiss, Frank Lindseth, Robertas Damaševičius
2022 PLoS ONE  
Convolutional neural networks may be suited for such tasks, but require substantial amounts of labeled data to train. Obtaining labeled data is a challenge, especially in the medical domain.  ...  Our model trained on a small number of semantically labeled data achieved a mean dice similarity coefficient of 71.0 on the MSD Lung dataset.  ...  Acknowledgments This work was conducted at the machines Idun [44] , Malvik (a cluster node owned and maintained by NTNU AI Lab), and Bohaga (a machine owned by NTNU, maintained by Gabriel Kiss).  ... 
doi:10.1371/journal.pone.0266147 pmid:35381046 pmcid:PMC8982833 fatcat:ktby5gbcr5bnzocuyzu2zvfg5u

Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies

Simon J. Doran, Mohammad Al Sa'd, James A. Petts, James Darcy, Kate Alpert, Woonchan Cho, Lorena Escudero Sanchez, Sachidanand Alle, Ahmed El Harouni, Brad Genereaux, Erik Ziegler, Gordon J. Harris (+4 others)
2022 Tomography  
; the integration of NVIDIA's Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed  ...  Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a "smart CT" paintbrush tool  ...  AIAA forms part of the Clara Application Framework [32] , recently updated to incorporate the Medical Open Network for AI (MONAI) PyTorch-based open-source framework for deep learning in healthcare imaging  ... 
doi:10.3390/tomography8010040 pmid:35202205 pmcid:PMC8875191 fatcat:wlfjdjvk3rfnzo3botrizwm53i

A Fast Method for Whole Liver- and Colorectal Liver Metastasis Segmentations from MRI Using 3D FCNN Networks

Yuliia Kamkova, Egidijus Pelanis, Atle Bjørnerud, Bjørn Edwin, Ole Jakob Elle, Rahul Prasanna Kumar
2022 Applied Sciences  
Furthermore, applying this method to a not-annotated dataset creates a complete 3D segmentation in less than 6 s per MRI volume, with a mean segmentation Dice score of 0.994 ± 0.003 for the liver and 0.709  ...  The liver is the most frequent organ for metastasis from colorectal cancer, one of the most common tumor types with a poor prognosis.  ...  The authors would like to thank Tomas Sakinis and David Aghayan for helping with dataset creation and segmentation, and Daniel Soule for the writing assistance for this paper.  ... 
doi:10.3390/app12105145 fatcat:4hlmickjdjbgpo757ejxprt2fu

Segmentation of Tricuspid Valve Leaflets From Transthoracic 3D Echocardiograms of Children With Hypoplastic Left Heart Syndrome Using Deep Learning

Christian Herz, Danielle F. Pace, Hannah H. Nam, Andras Lasso, Patrick Dinh, Maura Flynn, Alana Cianciulli, Polina Golland, Matthew A. Jolley
2021 Frontiers in Cardiovascular Medicine  
TV failure is thus associated with heart failure, and the outcome of TV valve repair are currently poor. 3D echocardiography (3DE) can generate high-quality images of the valve, but segmentation is necessary  ...  The FCN with the input of an annular curve achieved a median DSC of 0.86 [IQR: 0.81–0.88] and MBD of 0.35 [0.23–0.4] mm for the merged segmentation and an average DSC of 0.77 [0.73–0.81] and MBD of 0.6  ...  U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 .  ... 
doi:10.3389/fcvm.2021.735587 pmid:34957233 pmcid:PMC8696083 fatcat:f6xjcnvoxjhr7nqwz4qtcidrou

The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open data [article]

Tobias Fechter, Ilias Sachpazidis, Dimos Baltas
2022 arXiv   pre-print
Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role.  ...  Therefore, a second focus of this work was on the analysis of the availability of open source, open data and open models.  ...  Introduction In the treatment and diagnosis of cancer, medical images play an essential role.  ... 
arXiv:2205.07516v1 fatcat:mwin3gnexrg35bquqdeap25nea

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting

David Bouget, André Pedersen, Asgeir S. Jakola, Vasileios Kavouridis, Kyrre E. Emblem, Roelant S. Eijgelaar, Ivar Kommers, Hilko Ardon, Frederik Barkhof, Lorenzo Bello, Mitchel S. Berger, Marco Conti Nibali (+18 others)
2022 Frontiers in Neurology  
Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle.  ...  In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models.  ...  On the other hand, open-source tools are being developed to assist in image labeling and the generation of AI models for clinical evaluation, such as MONAI Label (33) or Biomedisa (34) .  ... 
doi:10.3389/fneur.2022.932219 fatcat:txz6aiak6bbtpnmwxzer4nlrla

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics

Thomas C. Booth, Evita C. Wiegers, Esther A. H. Warnert, Kathleen M. Schmainda, Frank Riemer, Ruben E. Nechifor, Vera C. Keil, Gilbert Hangel, Patrícia Figueiredo, Maria Álvarez-Torres, Otto M. Henriksen
2022 Frontiers in Oncology  
The body of evidence for clinical application of amide proton transfer imaging has been building for a decade, but more evidence is required to confirm chemical exchange saturation transfer use as a monitoring  ...  the potential for monitoring biomarkers was reviewed and individual modalities of metabolism and/or chemical composition imaging discussed.  ...  Medical Open Network for AI (MONAI) . Available at: https://monai.io/ (Accessed 30 Dec 2020). 122. Booth TC, Akpinar B, Roman A, Shuaib H, Luis A, Chelliah A, et al.  ... 
doi:10.3389/fonc.2021.811425 pmid:35340697 pmcid:PMC8948428 fatcat:3rquzdrsireutl3kky4njnboou

Image retrieval

Ritendra Datta, Dhiraj Joshi, Jia Li, James Z. Wang
2008 ACM Computing Surveys  
We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology.  ...  While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly  ...  In the MIL framework, a set of say l training images for learning an image category are conceived as labeled bags {(B 1 , y 1 ), ..., (B l , y l )}, where each bag B i is a collection of instances v ij  ... 
doi:10.1145/1348246.1348248 fatcat:5jbcrsxkkbac5cya3zb7eb22ea

Development and Visual Assessment of a Multi-Modality Brain Segmentation Pipeline

Caroline Magg, Renata Georgia Raidou
2021
Second, we design and implement an interactive web-based VA application for the assessment of algorithm performance and results.  ...  After developing an automated algorithm, artificial intelligence (AI) engineers must evaluate the results of their models against ground truth labels and compare them to other algorithms.  ...  Geurts et al. [42] published a method to compare and evaluate several statistical shape models for 3D medical image segmentation.  ... 
doi:10.34726/hss.2021.92822 fatcat:nh7eyl57rfh7npolqijoiwacei