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Evaluation Metrics for Medical Organ Segmentation and Lesion Detection [chapter]

Abdel Aziz Taha, Allan Hanbury
2017 Cloud-Based Benchmarking of Medical Image Analysis  
Furthermore, this chapter provides an overview of metrics used for the Lesion Detection Benchmark. Source code is available at: https://github.com/  ...  In particular, it provides an overview of 20 evaluation metrics for segmentation, from which four metrics were selected to be used in VISCERAL benchmarks.  ...  Evaluation Metrics for MedicalEvaluation Metrics for MedicalEvaluation Metrics for MedicalEvaluation Metrics for MedicalEvaluation Metrics for Medical …  ... 
doi:10.1007/978-3-319-49644-3_6 fatcat:kfahlnzduvfpbmjf5k5okdcngu

The Liver Tumor Segmentation Benchmark (LiTS) [article]

Patrick Bilic, Patrick Ferdinand Christ, Eugene Vorontsov, Grzegorz Chlebus, Hao Chen, Qi Dou, Chi-Wing Fu, Xiao Han, Pheng-Ann Heng, Jürgen Hesser, Samuel Kadoury, Tomasz Konopczyǹski (+44 others)
2019 arXiv   pre-print
The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI).  ...  In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International  ...  In section 3.5.3, we separate the Dice score from lesion detection by evaluating it only for each detected lesion, as a segmentation metric.  ... 
arXiv:1901.04056v1 fatcat:25ekt2znl5adnd5laap4ez6a4y

Ischemic Stroke Lesion Segmentation Challenge 2022: Acute, sub-acute and chronic stroke infarct segmentation [article]

Ezequiel De La Rosa, Uta Hanning, Jan Kirschke, Bjoern Menze, Mauricio Reyes, Roland Wiest, Benedikt Wiestler
2022 Zenodo  
This MICCAI 2022 challenge edition is organized as a two-task event, namely: 1) DWI infarct segmentation in acute and sub-acute stroke and 2) Single channel T1-weighted lesion segmentation in acute, sub-acute  ...  The ISLES'15 and ISLES'18 challenges played a crucial role in identifying prominent methods for acute and sub-acute ischemic stroke lesion segmentation.  ...  Challenge 2022: Acute, sub-acute and chronic stroke infarct segmentation Lesion-wise metrics: -Detection: Detection F1 -Count: Absolute difference Metrics are defined as follows: -Dice Similarity Coefficient  ... 
doi:10.5281/zenodo.6517002 fatcat:qewiebltqbc3plj4zom3ggzcai

Ischemic Stroke Lesion Segmentation Challenge 2022: Acute, sub-acute and chronic stroke infarct segmentation [article]

Ezequiel De La Rosa, Uta Hanning, Jan Kirschke, Bjoern Menze, Mauricio Reyes, Roland Wiest, Benedikt Wiestler
2022 Zenodo  
This MICCAI 2022 challenge edition is organized as a two-task event, namely: 1) DWI infarct segmentation in acute and sub-acute stroke and 2) Single channel T1-weighted lesion segmentation in acute, sub-acute  ...  The ISLES'15 and ISLES'18 challenges played a crucial role in identifying prominent methods for acute and sub-acute ischemic stroke lesion segmentation.  ...  Challenge 2022: Acute, sub-acute and chronic stroke infarct segmentation Lesion-wise metrics: -Detection: Detection F1 -Count: Absolute difference Metrics are defined as follows: -Dice Similarity Coefficient  ... 
doi:10.5281/zenodo.6362388 fatcat:bawxqtgl25gilcv7urh5hv5lru

SSEGEP: Small SEGment Emphasized Performance evaluation metric for medical image segmentation [article]

Ammu R, Neelam Sinha
2021 arXiv   pre-print
To address this, we propose a novel evaluation metric for segmentation performance, emphasizing smaller segments, by assigning higher weightage to smaller segment pixels.  ...  Automatic image segmentation is a critical component of medical image analysis, and hence quantifying segmentation performance is crucial.  ...  ACKNOWLEDGEMENTS We would like to thank Fabian Isensee for providing the segmentation results of pancreas and liver images that are obtained using nnU-Net [40] .  ... 
arXiv:2109.03435v1 fatcat:6xcigeddsrf75cjxsnpw4tvi3e

Multiple sclerosis new lesions segmentation challenge

Frédéric Cervenansky, Olivier Commowick, François Cotton, Michel Dojat, Edan), Gilles
2021 Zenodo  
Automating the detection of these new lesions would therefore be a major advance for evaluating the patient disease activity.  ...  One of the major challenges in using MRI for MS is the segmentation of lesions whose number and appearance at a given time point are crucial indicators for diagnostic and treatment follow-up.  ...  - Number of new lesions detected in sub-brain regions -Dice score for segmentation evaluation -Surface distance measure between lesions as in MICCAI 2016 challenge [5] Metrics for empty reference cases  ... 
doi:10.5281/zenodo.4575409 fatcat:zxyotpsbfng65f6grd6dsuyrjy

VISCERAL: Evaluation-as-a-Service for Medical Imaging [chapter]

Allan Hanbury, Henning Müller
2017 Cloud-Based Benchmarking of Medical Image Analysis  
MICCAI for medical imaging and ICPR for pattern recognition.  ...  Systematic evaluation has had a strong impact on many data analysis domains, for example, TREC and CLEF in information retrieval, ImageCLEF in image retrieval, and many challenges in conferences such as  ...  Participants in the Anatomy Benchmarks have the task of submitting software that automatically segments the organs for which manual segmentations are provided, or detecting the locations of the landmarks  ... 
doi:10.1007/978-3-319-49644-3_1 fatcat:fshbd44tnbgzvg57f3i3rjmnwq

A systematic review on the evaluation and characteristics of computer-aided diagnosis systems

Vagner Mendonça Gonçalves, Márcio Eduardo Delamaro, Fátima de Lourdes dos Santos Nunes
2014 Revista Brasileira de Engenharia Biomédica  
Images from computed tomographies and mammographies are the most encountered types of medical images. Additionally, a number of studies used public databases for CAD evaluations.  ...  The main evaluation metrics and methods applied to CAD systems include sensitivity, accuracy, specifi city and receiver operating characteristic (ROC) analyses.  ...  For example, to identify actual lesions from medical images, the actual details and characteristics of structures of lesions in an image must be known beforehand.  ... 
doi:10.1590/1517-3151.0517 fatcat:wvkbrgvw4fcj5jxbmvl3ivk3iy

VAscular Lesions DetectiOn [article]

Carole Sudre, Kimberlin Van Wijnen, Marius Groot, Florian Dubost, Marleen De Bruijne
2020 Zenodo  
This is the challenge design document for the "VAscular Lesions DetectiOn" Challenge, accepted for MICCAI 2021. Appropriate blood supply is essential to the healthy maintenance of brain tissue.  ...  This challenge aims at promoting the development of new solutions for the automated detection, differentiation and segmentation of such very sparse and small objects while leveraging the presence of weak  ...  At the level of enlarged PVS detection, both detection and segmentation are important aspects that must be covered by the evaluation metrics.  ... 
doi:10.5281/zenodo.3715641 fatcat:fr3fbahxwrhkzb6gqnovt65w6q

Cross-Domain Federated Learning in Medical Imaging [article]

Vishwa S Parekh, Shuhao Lai, Vladimir Braverman, Jeff Leal, Steven Rowe, Jay J Pillai, Michael A Jacobs
2021 arXiv   pre-print
We evaluated cross-domain federated learning for the tasks of object detection and segmentation across two different experimental settings: multi-modal and multi-organ.  ...  The result from our experiments on cross-domain federated learning framework were very encouraging with an overlap similarity of 0.79 for organ localization and 0.65 for lesion segmentation.  ...  Similarly, an object detection model built for localizing organs in a whole-body (WB) MRI would would share architectural (anatomical) similarities with object detection models built for localizing organs  ... 
arXiv:2112.10001v1 fatcat:f2qu6qsc2nhr5htqxjssfjpkde

Automated Lesion Segmentation in Whole-Body FDG-PET/CT [article]

Sergios Gatidis, Thomas Küstner, Michael Ingrisch, Matthias Fabritius, Clemens Cyran
2022 Zenodo  
However, despite these recent advances tumor lesion detection and segmentation in whole-body PET/CT is still a challenging task.  ...  Additional quantitative evaluation of PET information would potentially allow for more precise and individualized diagnostic decisions.  ...  Accurate detection and segmentation of FDG-avid tumor lesions in whole body FDG-PET/CT.  ... 
doi:10.5281/zenodo.6362493 fatcat:5lkflo5owjagbd7lt7tuloymvq

VAscular Lesions DetectiOn [article]

Carole Sudre, Kimberlin Van Wijnen, Marius Groot, Florian Dubost, Marleen De Bruijne
2020 Zenodo  
This is the challenge design document for the "VAscular Lesions DetectiOn" Challenge, accepted for MICCAI 2021. Appropriate blood supply is essential to the healthy maintenance of brain tissue.  ...  This challenge aims at promoting the development of new solutions for the automated detection, differentiation and segmentation of such very sparse and small objects while leveraging the presence of weak  ...  Multiple raters (relevant for segmentation, detection and volume metrics): For all test cases we have segmentations of two different raters.  ... 
doi:10.5281/zenodo.4600654 fatcat:n32ben5kx5g6fp7nklo3ludl3m

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure [article]

Olivier Commowick, Audrey Istace, Michael Kain, Baptiste Laurent, Florent Leray, Mathieu Simon, Sorina Camarasu-Pop, Pascal Girard, Roxana Améli, Jean-Christophe Ferré, Anne Kerbrat, Thomas Tourdias (+33 others)
2018 biorxiv/medrxiv   pre-print
This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner.  ...  This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-the-art algorithms, against a high-quality database of 53 MS cases coming  ...  Acknowledgements This work was partly funded by France Life Imaging (grant ANR-11-INBS-0006 from the French "Investissements d' Avenir" program) for funding and sponsoring the challenge.  ... 
doi:10.1101/367557 fatcat:jgeby63qcveubl4qkwdyi433l4

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

Olivier Commowick, Audrey Istace, Michaël Kain, Baptiste Laurent, Florent Leray, Mathieu Simon, Sorina Camarasu Pop, Pascal Girard, Roxana Améli, Jean-Christophe Ferré, Anne Kerbrat, Thomas Tourdias (+33 others)
2018 Scientific Reports  
This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner.  ...  This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming  ...  Acknowledgements This work was partly funded by France Life Imaging (grant ANR-11-INBS-0006 from the French "Investissements d' Avenir" program) for funding and sponsoring the challenge.  ... 
doi:10.1038/s41598-018-31911-7 pmid:30209345 pmcid:PMC6135867 fatcat:d66xifhtejbqbazyiksj5nx6nm

Multilevel image recognition using discriminative patches and kernel covariance descriptor

Le Lu, Jianhua Yao, Evrim Turkbey, Ronald M. Summers, Stephen Aylward, Lubomir M. Hadjiiski
2014 Medical Imaging 2014: Computer-Aided Diagnosis  
/encoding, and similarity metrics for classification or matching.  ...  We extensively evaluate two extended Gaussian kernels using affine-invariant Riemannian metric or log-Euclidean metric with support vector machines (SVM), on two medical image classification problems of  ...  INTRODUCTION There has been much progress in structured, model based 3D organ segmentation [7, 8, 9, 10] in medical imaging for the last decade.  ... 
doi:10.1117/12.2043692 dblp:conf/micad/LuYTS14 fatcat:cyb6xud3lvhctlitwtv6k7ciem
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