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Tuning for Tissue Image Segmentation Workflows for Accuracy and Performance [article]

Luis F. R. Taveira, Tahsin Kurc, Alba C. M. A. Melo, Jun Kong, Erich Bremer, Joel H. Saltz, George Teodoro
2018 arXiv   pre-print
We propose a software platform that integrates methods and tools for multi-objective parameter auto- tuning in tissue image segmentation workflows.  ...  Input parameters in many nucleus segmentation workflows affect segmentation accuracy and have to be tuned for optimal performance.  ...  When the workflow used Table 2 . Results for the multi-objective auto-tuning as compared to the application default parameters.  ... 
arXiv:1810.02911v1 fatcat:vrdg5hxzvbds5cslcplm5rto5y

Efficient Methods and Parallel Execution for Algorithm Sensitivity Analysis with Parameter Tuning on Microscopy Imaging Datasets [article]

George Teodoro, Tahsin Kurc, Luis F. R. Taveira, Alba C. M. A. Melo, Jun Kong, Joel Saltz
2016 arXiv   pre-print
We describe an informatics framework for researchers and clinical investigators to efficiently perform parameter sensitivity analysis and auto-tuning for algorithms that segment and classify image features  ...  Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies, and auto-tuning with large datasets with the use of high-performance systems and techniques.  ...  A workflow optimized for a group of images or a type of tissue may not perform well for other tissue types or images.  ... 
arXiv:1612.03413v1 fatcat:iny3igenvncgxoe7paike7f36e

Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines

George Teodoro, Tahsin M. Kurç, Luís F. R. Taveira, Alba C. M. A. Melo, Yi Gao, Jun Kong, Joel H. Saltz
2017 Bioinformatics  
They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters.  ...  Motivation: Sensitivity analysis and parameter tuning are important processes in large-scale image analysis.  ...  In the auto-tuning process, image analysis results (i.e. sets of segmented objects in our case) generated from a set of input parameter values are compared to the reference dataset.  ... 
doi:10.1093/bioinformatics/btw749 pmid:28062445 pmcid:PMC5409344 fatcat:3ack23wpmbgydl6zaay3bcn7va

The Allen Cell Structure Segmenter: a new open source toolkit for segmenting 3D intracellular structures in fluorescence microscopy images [article]

Jianxu Chen, Liya Ding, Matheus P. Viana, Melissa C. Hendershott, Ruian Yang, Irina A. Mueller, Susanne M. Rafelski
2018 bioRxiv   pre-print
The Allen Cell Structure Segmenter consists of two complementary elements, a classic image segmentation workflow with a restricted set of algorithms and parameters and an iterative deep learning segmentation  ...  Two straightforward "human-in-the-loop" curation strategies convert a set of classic image segmentation workflow results into a set of 3D ground truth images for iterative model training without the need  ...  We especially thank the Allen Institute for Cell Science Gene Editing, Assay Development, Microscopy, and Pipeline teams for providing cell lines and images.  ... 
doi:10.1101/491035 fatcat:huzf5xm75fd2deifrvhawswr2i

Fully automated treatment planning for MLC‐based robotic radiotherapy

B.W.K. Schipaanboord, M.K. Giżyńska, L. Rossi, K.C. de Vries, B.J.M. Heijmen, S. Breedveld
2021 Medical Physics (Lancaster)  
To propose and validate a fully automated multi-criterial treatment planning solution for a CyberKnife® equipped with an InCiseTM 2 multi-leaf collimator.  ...  Both the AUTO MCO and segmentation algorithms have been in-house developed. AUTO MCO generates for each patient a single, high-quality Pareto-optimal IMRT plan.  ...  for healthy tissues, generally 95%.  ... 
doi:10.1002/mp.14993 pmid:34037258 fatcat:tau2erzns5bgpbp44pg24f7jaq

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery

Yan Liu, Strahinja Stojadinovic, Brian Hrycushko, Zabi Wardak, Steven Lau, Weiguo Lu, Yulong Yan, Steve B. Jiang, Xin Zhen, Robert Timmerman, Lucien Nedzi, Xuejun Gu (+1 others)
2017 PLoS ONE  
We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients  ...  In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets.  ...  Automatic delineation workflow The auto-segmentation workflow that we developed is illustrated in Fig 1.  ... 
doi:10.1371/journal.pone.0185844 pmid:28985229 pmcid:PMC5630188 fatcat:64c577egijaohhndrd3kcjawfy

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.  ...  ArXiv was searched for papers mentioning one of a set of terms related to medical imaging.  ... 
doi:10.1016/j.media.2017.07.005 pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

Deep Learning in Multi-organ Segmentation [article]

Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications.  ...  We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge  ...  and Neck Auto-segmentation Challenge [105] , which provide a benchmark dataset and platform for evaluating performance of automatic multi-organ segmentation methods of in head & neck CT images.  ... 
arXiv:2001.10619v1 fatcat:6uwqwnzydzccblh5cajhsgdpea

Quantifying drug tissue biodistribution by integrating high content screening with deep-learning analysis

Zhuyin Li, Youping Xiao, Jia Peng, Darren Locke, Derek Holmes, Lei Li, Shannon Hamilton, Erica Cook, Larnie Myer, Dana Vanderwall, Normand Cloutier, Akbar M. Siddiqui (+4 others)
2020 Scientific Reports  
We have developed a multiplexed and high-throughput method to quantify drug distribution in tissues by integrating high content screening (HCS) with U-Net based deep learning (DL) image analysis models  ...  This technology combination allowed direct visualization and quantification of biologics drug binding in targeted tissues with cellular resolution, thus enabling biologists to objectively determine drug  ...  Acknowledgements We are grateful to the following individuals for their contributions to the technology described in this manu-  ... 
doi:10.1038/s41598-020-71347-6 pmid:32873881 fatcat:yagj7jrsrrbizj3r7mshziq4ae

Bots for Software-Assisted Analysis of Image-Based Transcriptomics [article]

Marcelo Cicconet, Daniel R. Hochbaum, David Richmond, Bernardo L. Sabatini
2017 bioRxiv   pre-print
We introduce software assistants -- bots -- for the task of analyzing image-based transcriptomic data.  ...  Our main release offers two algorithms for nuclei segmentation, and two for spot detection, to handle data of different complexities.  ...  Workflow and Algorithms Each choice leads to subsequent mini-apps where parameters can be tested, except for the Machine Learning option, where the user is asked to load a trained model for nuclei segmentation  ... 
doi:10.1101/172296 fatcat:4gi44kxko5hfloxvhbtwrwyfyi

Bots for Software-Assisted Analysis of Image-Based Transcriptomics

Marcelo Cicconet, Daniel R. Hochbaum, David L. Richmond, Bernardo L. Sabatin
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
We introduce software assistants -bots -for the task of analyzing image-based transcriptomic data.  ...  Our main release offers two algorithms for nuclei segmentation, and two for spot detection, to handle data of different complexities.  ...  Workflow and Algorithms Each choice leads to subsequent mini-apps where parameters can be tested, except for the Machine Learning option, where the user is asked to load a trained model for nuclei segmentation  ... 
doi:10.1109/iccvw.2017.24 dblp:conf/iccvw/CicconetHRS17 fatcat:5nwixsujbffhhfugzt2kelolbm

A tomographic workflow to enable deep learning for X-ray based foreign object detection [article]

Mathé T. Zeegers, Tristan van Leeuwen, Daniël M. Pelt, Sophia Bethany Coban, Robert van Liere, Kees Joost Batenburg
2022 arXiv   pre-print
X-ray imaging is a fast, non-invasive and widely applicable method for foreign object detection.  ...  Moreover, for real experimental data we show that the workflow leads to higher foreign object detection accuracies than with standard radiograph annotation.  ...  The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.  ... 
arXiv:2201.12184v1 fatcat:qgtw4v3i6fgnphlk3csah4xica

Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors [article]

Arnaud Boutillon, Pierre-Henri Conze, Christelle Pons, Valérie Burdin, Bhushan Borotikar
2022 arXiv   pre-print
We evaluate our contributions for performing bone segmentation using three scarce and pediatric imaging datasets of the ankle, knee, and shoulder joints.  ...  for morphological evaluation.  ...  step in the medical image analysis workflow guiding clinical decisions.  ... 
arXiv:2207.13502v1 fatcat:qe4stepkqnd2rjgtii4cwxsk34

OpSeF: Open Source Python Framework for Collaborative Instance Segmentation of Bioimages

Tobias M. Rasse, Réka Hollandi, Peter Horvath
2020 Frontiers in Bioengineering and Biotechnology  
We provide Jupyter notebooks that document sample workflows based on various image collections.  ...  OpSeF streamlines the optimization of parameters for pre- and postprocessing such, that an available model may frequently be used without retraining.  ...  ACKNOWLEDGMENTS We would like to thank Thorsten Falk and Volker Hilsenstein (Monash Micro Imaging) for testing of the software and critical comments on the manuscript and the IT service group of the MPI  ... 
doi:10.3389/fbioe.2020.558880 pmid:33117778 pmcid:PMC7576117 fatcat:wnok2f724re4hlky5zgrqivave

OpSeF IV: Open source Python framework for segmentation of biomedical images [article]

Tobias Manuel Rasse, Reka Hollandi, Peter Horvath
2020 bioRxiv   pre-print
The optimization of parameters used for preprocessing and selection of a suitable model for segmentation form one functional unit.  ...  They can be refined by object-selection based on their region properties, a biomedical-user-provided or an auto-generated mask.  ...  Acknowledgments We would like to thank Thorsten Falk and Volker Hilsenstein (Monash Micro Imaging) for testing of the software and critical comments on the manuscript; the IT service group of the MPI Heart  ... 
doi:10.1101/2020.04.29.068023 fatcat:qejkbjayizaflk6mza67uo7gwe
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