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Towards Deep Cellular Phenotyping in Placental Histology [article]

Michael Ferlaino, Craig A. Glastonbury, Carolina Motta-Mejia, Manu Vatish, Ingrid Granne, Stephen Kennedy, Cecilia M. Lindgren, Christoffer Nellåker
2018 arXiv   pre-print
In this work, we present an open sourced, computationally tractable deep learning pipeline to analyse placenta histology at the level of the cell.  ...  We envisage that the automation of this pipeline to population scale studies of placenta histology has the potential to improve our understanding of basic cellular placental biology and its variations,  ...  In this work we start addressing some of these fundamental issues by developing a deep learning pipeline to automate the analysis of cell phenotyping in placenta histology.  ... 
arXiv:1804.03270v2 fatcat:2nf3tgk2g5do7hsrvl2n7zf47u

Towards computer-assisted TTTS: Laser ablation detection for workflow segmentation from fetoscopic video

Francisco Vasconcelos, Patrick Brandão, Tom Vercauteren, Sebastien Ourselin, Jan Deprest, Donald Peebles, Danail Stoyanov
2018 International Journal of Computer Assisted Radiology and Surgery  
We adopt a deep learning approach, specifically the ResNet101 architecture, for classification of different surgical actions performed during laser ablation therapy.  ...  Our results show that deep learning methods are a promising approach for ablation detection.  ...  Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest. Ethical approval For this type of study formal consent is not required.  ... 
doi:10.1007/s11548-018-1813-8 pmid:29951938 fatcat:hni3b6mjnbci5gjbwxumb4cnzy

New Frontiers in Placenta Tissue Imaging

2020 EMJ Radiology  
Thus, normal placenta anatomy and physiology is absolutely required for maintenance of maternal and fetal health during pregnancy.  ...  Effective noninvasive imaging and image analysis are needed to advance the obstetrician's clinical reasoning toolkit and improve the utility of the placenta in interpreting maternal and fetal health trajectories  ...  Finally, in the last section of this review the authors discuss how advanced computational approaches such as computer vision, automation, and deep learning strengthen the power of preclinical and clinical  ... 
doi:10.33590/emjradiol/19-00210 fatcat:arzxympqjjglfgj772jht3bn2a

Slic-Seg: A minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views

Guotai Wang, Maria A. Zuluaga, Rosalind Pratt, Michael Aertsen, Tom Doel, Maria Klusmann, Anna L. David, Jan Deprest, Tom Vercauteren, Sébastien Ourselin
2016 Medical Image Analysis  
Segmentation of the placenta from fetal MRI is challenging due to sparse acquisition, inter-slice motion, and the widely varying position and shape of the placenta between pregnant women.  ...  with low intra-and interoperator variability; 3) higher accuracy than state-of-the-art interactive segmentation methods; and 4) an improved accuracy due to the co-segmentation based refinement, which  ...  Acknowledgments This work was supported through an Innovative Engineering for Health award by the Wellcome Trust ( WT101957 ); Engineering and Physical Sciences Research Council (EPSRC) (NS/A0 0 0 027/  ... 
doi:10.1016/ pmid:27179367 pmcid:PMC5052128 fatcat:mzml33bznndnreavfhcn5kjiuy

Placenta Imaging Workshop 2018 report: Multiscale and multimodal approaches

Paddy Slator, Rosalind Aughwane, Georgina Cade, Daniel Taylor, Anna L. David, Rohan Lewis, Eric Jauniaux, Adrien Desjardins, Laurent J. Salomon, Anne-Elodie Millischer, Vassilis Tsatsaris, Mary Rutherford (+60 others)
2019 Placenta  
over 20 posters with subjects ranging from the 33 movement of blood within the placenta to the efficient segmentation of fetal MRI using 34 deep learning tools.  ...  The Centre for Medical Image Computing (CMIC) at University College London 27 (UCL) hosted a two-day workshop on placenta imaging on April 12 th and 13 th 2018.  ...  The technique applied to the placenta allows the 169 three-dimensional chorionic and deep branching vessel structure to be visualised and 170 quantified, and can transform our understanding and appreciation  ... 
doi:10.1016/j.placenta.2018.10.010 pmid:30396518 fatcat:cb6zwafzxfb67kypndyjm56l4q

FetNet: a recurrent convolutional network for occlusion identification in fetoscopic videos

Sophia Bano, Francisco Vasconcelos, Emmanuel Vander Poorten, Tom Vercauteren, Sebastien Ourselin, Jan Deprest, Danail Stoyanov
2020 International Journal of Computer Assisted Radiology and Surgery  
We adapt an existing CNN architecture for spatial feature extraction and integrated it with the LSTM network for end-to-end spatio-temporal inference.  ...  We introduce differential learning rates during the model training to effectively utilising the pre-trained CNN weights.  ...  In the case of TTTS laser therapy, deep learning methods have only been marginally explored.  ... 
doi:10.1007/s11548-020-02169-0 pmid:32350787 fatcat:g5cpebfovvg4zfaymiofolfnaq

Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images

Naveen Paluru, Aveen Dayal, Havard Bjorke Jenssen, Tomas Sakinis, Linga Reddy Cenkeramaddi, Jaya Prakash, Phaneendra K. Yalavarthy
2021 IEEE Transactions on Neural Networks and Learning Systems  
The development of automated methods for quantifying COVID-19 abnormalities in these CT images is invaluable to clinicians.  ...  The hallmark of COVID-19 in chest CT images is the presence of ground-glass opacities in the lung region, which are tedious to segment manually.  ...  ACKNOWLEDGMENT The authors are thankful to Dr. Johannes Hofmanninger for providing the lung masks as open-source, which were utilized in this work.  ... 
doi:10.1109/tnnls.2021.3054746 fatcat:7gixbvbmvbg4dahq4sjqletbgq

Combining deep learning with anatomy analysis for segmentation of portal vein for liver SBRT planning

Bulat Ibragimov, Diego Toesca, Daniel Chang, Albert Koong, Lei Xing
2017 Physics in Medicine and Biology  
In this paper, we propose a novel framework for automated PV segmentation from computed tomography (CT) images.  ...  Automated segmentation of portal vein (PV) for liver radiotherapy planning is a challenging task due to potentially low vasculature contrast, complex PV anatomy and image artifacts originated from fiducial  ...  Section 2 presents the methodology of convolutional neural networks, i.e. a concept of deep learning for image analysis, PV centerline detecting and combining deep learning with centerlines for PV segmentation  ... 
doi:10.1088/1361-6560/aa9262 pmid:28994665 pmcid:PMC5739057 fatcat:57mfdxpvmfdshhykm7bayviqce

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

Masaaki Komatsu, Akira Sakai, Ai Dozen, Kanto Shozu, Suguru Yasutomi, Hidenori Machino, Ken Asada, Syuzo Kaneko, Ryuji Hamamoto
2021 Biomedicines  
We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and  ...  The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/biomedicines9070720 fatcat:aj5jsjjglbhfnhhzslnkp5zahy

Stem cell imaging through convolutional neural networks: current issues and future directions in artificial intelligence technology

Ramanaesh Rao Ramakrishna, Zariyantey Abd Hamid, Wan Mimi Diyana Wan Zaki, Aqilah Baseri Huddin, Ramya Mathialagan
2020 PeerJ  
In particular, deep learning, a subfield of AI, offers an automated platform for analyzing iPSC colonies and other colony-forming stem cells.  ...  Deep learning rectifies data features using a convolutional neural network (CNN), a type of multi-layered neural network that can play an innovative role in image recognition.  ...  ACKNOWLEDGEMENTS The authors would like to thank the Biotechnology Laboratory, Faculty of Health Sciences, UKM for providing facilities to conduct this research.  ... 
doi:10.7717/peerj.10346 pmid:33240655 pmcid:PMC7680049 fatcat:ej7vg2gukbdcrahelaw2cpofc4

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area.  ...  Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.  ...  Acknowledgments The authors would like to thank members of the Diagnostic Image Analysis Group for discussions and suggestions.  ... 
doi:10.1016/ pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

Stain-free Detection of Embryo Polarization using Deep Learning [article]

Cheng Shen, Adiyant Lamba, Meng Zhu, Ray Zhang, Changhuei Yang, Magdalena Zernicka Goetz
2021 arXiv   pre-print
By compressing three-dimensional time-lapsed image data into two-dimensions, we are able to reduce data to an easily manageable size for deep learning processing.  ...  We discovered that our self-learning model focuses upon the angle between cells as one known cue for compaction, which precedes polarization, but it outperforms the use of this cue alone.  ...  Acknowledgements We thank all colleagues in the C.Y. and M.Z.-G. labs for helpful suggestions and feedback. We also thank all human volunteers.  ... 
arXiv:2111.05315v1 fatcat:yquvpjx4tzhhho47hsv3czwxuy

Going to Extremes: Weakly Supervised Medical Image Segmentation

Holger R. Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, Daguang Xu
2021 Machine Learning and Knowledge Extraction  
Our approach has the potential to speed up the process of generating new training datasets for the development of new machine-learning and deep-learning-based models for, but not exclusively, medical image  ...  Medical image annotation is a major hurdle for developing precise and robust machine-learning models.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/make3020026 fatcat:vpy3rtl63rctjcv3sgtd7mn56u

A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta [article]

Win M. Tun, Gowsihan Poologasundarampillai, Helen Bischof, Gareth Nye, Oliver N.F. King, Mark Basham, Yasuaki Tokudome, Rohan M. Lewis, Edward D. Johnstone, Paul Brownbill, Michele Darrow, Igor L. Chernyavsky
2020 bioRxiv   pre-print
Using the human placenta as an example, this study brings together sophisticated sample preparation protocols, advanced imaging, and robust, validated machine-learning segmentation techniques to provide  ...  The developed protocol is beneficial for high-throughput investigation of structure-function relationships in both normal and diseased placentas, allowing us to optimise therapeutic approaches for pathological  ...  Automatic 3D ultrasound segmentation of the first trimester placenta using 18 deep learning. In: 2017 IEEE 14th International Symposium on Biomedical 19 Imaging (ISBI 2017). 2017. p. 279-82.  ... 
doi:10.1101/2020.12.07.411462 fatcat:vfpkroixgve4vhd2reygoxgfaa

ClusterMap: multi-scale clustering analysis of spatial gene expression [article]

Yichun He, Xin Tang, Jiahao Huang, Haowen Zhou, Kevin Chen, Albert Liu, Jingyi Ren, Hailing Shi, Zuwan Lin, Qiang Li, Abhishek Aditham, Jian Shu (+2 others)
2021 bioRxiv   pre-print
However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data.  ...  transcriptomic images.  ...  Xiaole Shirley Liu and Jane Salant for their helpful comments to the manuscript.  ... 
doi:10.1101/2021.02.18.431337 fatcat:s2l5xkwrxjdrvmabuoypqbjzwi
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