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Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations [article]

Ayman Al-Kababji, Faycal Bensaali, Sarada Prasad Dakua, Yassine Himeur
2022 arXiv   pre-print
Hence, in this paper, we survey the key studies that are published between 2014 and 2022, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic tumors  ...  Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and they are further partitioned if the amount of work that falls under a certain scheme is significant  ...  The contents herein are solely the responsibility of the authors.  ... 
arXiv:2103.06384v2 fatcat:w6dxpyxhzzhs3gel25pgy6fqke

Brain Image Segmentation in Recent Years: A Narrative Review

Ali Fawzi, Anusha Achuthan, Bahari Belaton
2021 Brain Sciences  
Brain image segmentation is one of the most time-consuming and challenging procedures in a clinical environment. Recently, a drastic increase in the number of brain disorders has been noted.  ...  Common issues, advantages, and disadvantages of brain image segmentation methods are also discussed to provide a better understanding of the strengths and limitations of existing methods.  ...  Conclusions This paper presents a critical review of the trending approaches to brain segmentation.  ... 
doi:10.3390/brainsci11081055 fatcat:cdie3nuxzzfevoynik3iqtenli

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation [article]

Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, Xiaowei Ding
2020 arXiv   pre-print
Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated  ...  In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results.  ...  The segmentation model is a U-Net that receives as input the image stacked with the foreground and background attention maps, where attention maps are constructed by placing a Gaussian blob at each foreground  ... 
arXiv:1908.10454v2 fatcat:mjvfbhx75bdkbheysq3r7wmhdi

CARS 2020—Computer Assisted Radiology and Surgery Proceedings of the 34th International Congress and Exhibition, Munich, Germany, June 23–27, 2020

2020 International Journal of Computer Assisted Radiology and Surgery  
Preface Analogue and Digital CARS 2020 Congress The overarching purpose of the scholarly publication and communication process of IJCARS in the context of the CARS congress could be defined as: "To enable  ...  A hybrid (analogue and digital) CARS 2020 has therefore been envisaged to take place at the University Hospital in Munich, with a balanced combination of analogue/personal and digital presentations and  ...  Besides, use of gloves is low, so doses of hands are still high. Therefore, a master-slave robotic system for VI is necessary for minimization of the radiation exposure.  ... 
doi:10.1007/s11548-020-02171-6 pmid:32514840 fatcat:lyhdb2zfpjcqbf4mmbunddwroq

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review [article]

Juan J. Cerrolaza, Mirella Lopez-Picazo, Ludovic Humbert, Yoshinobu Sato, Daniel Rueckert, Miguel Angel Gonzalez Ballester, Marius George Linguraru
2018 arXiv   pre-print
The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution  ...  In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology.  ...  Acknowledgements This paper was supported in part by the Marie Skodoska-Curie Actions of the UE Framework Program for Research and Innovation, under REA grant agreement 706372.  ... 
arXiv:1812.08577v1 fatcat:xjw2g25pxfftpnduss6d5sggzu

Deep learning in medical imaging and radiation therapy

Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski, Xiaosong Wang, Karen Drukker, Kenny H. Cha, Ronald M. Summers, Maryellen L. Giger
2018 Medical Physics (Lancaster)  
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies  ...  that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations.  ...  The same research group in their later research added a context-aware GAN for improved results. 259 Han et al. 256 adopted and modified the U-net architecture for sCT generation from MRI.  ... 
doi:10.1002/mp.13264 pmid:30367497 fatcat:bottst5mvrbkfedbuocbrstcnm

GAN-based generation of realistic 3D data: A systematic review and taxonomy [article]

André Ferreira, Jianning Li, Kelsey L. Pomykala, Jens Kleesiek, Victor Alves, Jan Egger
2022 arXiv   pre-print
In this review, we provide a summary of works that generate realistic 3D synthetic data using GANs.  ...  In non-medical fields, the high cost of obtaining a sufficient amount of high-quality data can also be a concern.  ...  Acknowledgement This work received funding from enFaced (FWF KLI 678), enFaced 2.0 (FWF KLI 1044) and KITE (Plattform für KI-Translation Essen) from the REACT-EU initiative (https://kite.ikim.nrw/).  ... 
arXiv:2207.01390v1 fatcat:yny6btsy5zemjnbk7lnmxgsyzy

Class-Aware Generative Adversarial Transformers for Medical Image Segmentation [article]

Chenyu You, Ruihan Zhao, Fenglin Liu, Sandeep Chinchali, Ufuk Topcu, Lawrence Staib, James S. Duncan
2022
semantic contexts and anatomical textures.  ...  We then design a novel class-aware transformer module to better learn the discriminative regions of objects with semantic structures.  ...  ., CATformer and CA-GANformer) with previous state-of-the-art transformerbased segmentation methods, including U-Net (Ronneberger et al., 2015) , AttnUNet (Schlemper et al., 2019) , ResNet50 + U-Net  ... 
doi:10.48550/arxiv.2201.10737 fatcat:mkzvtlsbfrhkdma3frfi3atnse

Proceedings of the World Molecular Imaging Congress 2021, October 5-8, 2021: General Abstracts

2022 Molecular Imaging and Biology  
In general, the values of CNR reached a plateau at around 8 iterations with an average improvement factor of about 1.7 for processed MRI images.  ...  For most clinical MRI cases, the total number of iterations for enhanced image quality is around 8 with a total number of resolution subsets around 4.  ...  The nanocluster system with a high selectivity showed the potential for fluorescence imaging and the integrating of gold and MMAE demonstrated excellent concurrent chemotherapy-radiotherapy efficacy, which  ... 
doi:10.1007/s11307-021-01693-y pmid:34982365 pmcid:PMC8725635 fatcat:4sfb3isoyfdhfbiwxfr55gvqym