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Biomedical Image Segmentation via Representative Annotation

Hao Zheng, Lin Yang, Jianxu Chen, Jun Han, Yizhe Zhang, Peixian Liang, Zhuo Zhao, Chaoli Wang, Danny Z. Chen
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this paper, we propose representative annotation (RA), a new deep learning framework for reducing annotation effort in biomedical image segmentation.  ...  Deep learning has been applied successfully to many biomedical image segmentation tasks.  ...  Introduction Image segmentation is a central task in diverse biomedical imaging applications.  ... 
doi:10.1609/aaai.v33i01.33015901 fatcat:nzxzxanru5fondrgamzrnq2hlq

Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative Learning [article]

Yang Nan, Gianmarc Coppola, Qiaokang Liang, Kunglin Zou, Wei Sun, Dan Zhang, Yaonan Wang, Guanzhen Yu
2017 arXiv   pre-print
Recent advances in deep learning have produced inspiring results on biomedical image segmentation, while its outcome is reliant on comprehensive annotation.  ...  In this paper, a reiterative learning framework was presented to train our network on partial annotated biomedical images, and superior performance was achieved without any pre-trained or further manual  ...  Compared with the annotation of the natural image segmentation problem, biomedical image segmentation data requires professional labeling and a great deal of patience.  ... 
arXiv:1712.07488v1 fatcat:ocmrrzedgzh2ncdn4yltl5fgfe

U-Net-and-a-half: Convolutional network for biomedical image segmentation using multiple expert-driven annotations [article]

Yichi Zhang, Jesper Kers, Clarissa A. Cassol, Joris J. Roelofs, Najia Idrees, Alik Farber, Samir Haroon, Kevin P. Daly, Suvranu Ganguli, Vipul C. Chitalia, Vijaya B. Kolachalama
2021 arXiv   pre-print
biomedical image segmentation.  ...  Development of deep learning systems for biomedical segmentation often requires access to expert-driven, manually annotated datasets.  ...  generalizable frameworks for biomedical segmentation.  ... 
arXiv:2108.04658v1 fatcat:ciudjgg4r5d5tlufvq6ws24qti

Quantification of Uncertainties in Biomedical Image Quantification 2021 [article]

Bjoern Menze, Leo Joskowicz, Spyridon Bakas, Andras Jakab, Ender Konukoglu, Anton Becker, Amber Simpson, Richard Do
2021 Zenodo  
directly inferred from human expert annotations.  ...  This variability, that is a property of the biological problem, the imaging modality, and the expert annotators, is – as of now - not sufficiently considered in the design of computerized algorithms for  ...  Examples: Training and test cases both represent a CT image of a human brain.  ... 
doi:10.5281/zenodo.4575204 fatcat:uw7dsqvplrftplxcsmrro4gmta

Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss [article]

Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Pheng-Ann Heng
2018 arXiv   pre-print
In this paper, we propose an unsupervised domain adaptation framework with adversarial learning for cross-modality biomedical image segmentations.  ...  The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions.  ...  The backbone of our segmenter is the residual network for pixelwise prediction of biomedical images.  ... 
arXiv:1804.10916v2 fatcat:lzdnmkhtsrgezlxrbricuxu6nq

Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss

Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Pheng-Ann Heng
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
In this paper, we propose an unsupervised domain adaptation framework with adversarial learning for cross-modality biomedical image segmentations.  ...  The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions.  ...  The backbone of our segmenter is the residual network for pixelwise prediction of biomedical images.  ... 
doi:10.24963/ijcai.2018/96 dblp:conf/ijcai/DouOCCH18 fatcat:tkhu6wx3ojf7vcss43ixivbgkq

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
image datasets.  ...  It allows researchers to readily deploy their apps as services, which can be made available to clinicians via their preferred user-interface.  ...  Biomedisa has significant advantages over CPU-based semi-automatic segmentation tools for biomedical image analysis.  ... 
arXiv:2203.12362v1 fatcat:imdyysslozdp3meoi3f7njhyiq

Ontological labels for automated location of anatomical shape differences

Shane Steinert-Threlkeld, Siamak Ardekani, Jose L.V. Mejino, Landon Todd Detwiler, James F. Brinkley, Michael Halle, Ron Kikinis, Raimond L. Winslow, Michael I. Miller, J. Tilak Ratnanather
2012 Journal of Biomedical Informatics  
A method for automated location of shape differences in diseased anatomical structures via high resolution biomedical atlases annotated with labels from formal ontologies is described.  ...  In particular, a high resolution magnetic resonance image of the myocardium of the human left ventricle was segmented and annotated with structural terms from an extracted subset of the Foundational Model  ...  We would like to thank Geoffrey Gunther for assistance on the segmentation of the atlas image and Marianne Shaw for providing the vSPARQL library and related technical assistance.  ... 
doi:10.1016/j.jbi.2012.02.013 pmid:22490168 pmcid:PMC3371096 fatcat:ugfukdkwyvcefnxgqpvb7gw4bq

2021 Kidney and Kidney Tumor Segmentation Challenge [article]

Nicholas Heller, Nikolaos Papanikolopoulos, Christopher Weight
2020 Zenodo  
The KiTS19 challenge introduced the first large-scale public dataset of kidney and kidney tumor semantic segmentations, representing a considerable step towards reliable automatic segmentation of these  ...  Unfortunately, it was limited in both the scope of the dataset and the structures that were annotated.  ...  Segmentation • Tracking • Segmentation. 2021 Kidney and Kidney Tumor Segmentation Challenge Page 10 of 12 Biomedical Image Analysis ChallengeS (BIAS) Initiative Biomedical Image Analysis  ... 
doi:10.5281/zenodo.4674397 fatcat:g4caqxxdk5flxcsdsu25sle7mm

Endoscopic Vision Challenge 2021 [article]

Stefanie Speidel, Lena Maier-Hein, Danail Stoyanov, Sebastian Bodenstedt, Martin Wagner, Beat Müller, Jonathan Chen, Benjamin Müller, Franziska Mathis-Ullrich, Paul Scheikl, Jorge Bernal, Aymeric Histache (+18 others)
2021 Zenodo  
Furthermore, other surgical disciplines rely on microscopic images or use flexible endoscopes for diagnostic purposes.  ...  Algorithms that have been reported for such images include 3D surface reconstruction, salient feature motion tracking, instrument detection or activity recognition.  ...  Additional points: -Find image segmentation algorithms that can segment different objects and structures in Endoscopic Vision Challenge 2021 Page 8 of 66 Biomedical Image Analysis ChallengeS (BIAS) Initiative  ... 
doi:10.5281/zenodo.4572973 fatcat:njsq4tsqd5brte56zclucdh7su

Cross-Modality Domain Adaptation for Medical Image Segmentation [article]

Reuben Dorent, Aaron Kujawa, Jonathan Shapey, Samuel Joutard, Jorge Cardoso, Marc Modat, Nicola Rieke, Ben Glocker, Spyridon Bakas, Tom Vercauteren
2021 Zenodo  
The training source and target sets are respectively unpaired annotated ceT1 and non-annotated hrT2 scans.  ...  While contrast-enhanced T1 (ceT1) Magnetic Resonance Imaging (MRI) scans are commonly used for VS segmentation, recent work [1,2] has demonstrated that high-resolution T2 (hrT2) imaging could be a reliable  ...  Both training and test cases are annotated with survival (binary) 5 years after (first) image was taken. • The training, validation and test cases represent MR images of a human brain.  ... 
doi:10.5281/zenodo.4573118 fatcat:t6nk3pndgzeo5dlpox672e2gnu

AISO: Annotation of Image Segments with Ontologies

Nikhil Lingutla, Justin Preece, Sinisa Todorovic, Laurel Cooper, Laura Moore, Pankaj Jaiswal
2014 Journal of Biomedical Semantics  
Results: We developed a novel image segmentation and annotation software application, "Annotation of Image Segments with Ontologies" (AISO).  ...  In the future, quality annotated image segments may provide training data sets for developing machine learning applications for automated image annotation.  ...  Stockey at Oregon State University, for their contribution of the paleo-botanical image and subsequent segmentation and annotation, as described in the Case Studies and Figure 3 .  ... 
doi:10.1186/2041-1480-5-50 pmid:25584184 pmcid:PMC4290088 fatcat:77xwkdwl7ragxbvkuj2xywyfja

Stitched Multipanel Biomedical Figure Separation

K.C. Santosh, Sameer Antani, George Thoma
2015 2015 IEEE 28th International Symposium on Computer-Based Medical Systems  
Since such figures may comprise images from different imaging modalities, separating them is a critical first step for effective biomedical content-based image retrieval (CBIR).  ...  The method applies local line segment detection based on the graylevel pixel changes.  ...  Index Terms-Automation; line segment detection; stitched multipanel figures; biomedical publications; content-based image retrieval. I. INTRODUCTION A.  ... 
doi:10.1109/cbms.2015.51 dblp:conf/cbms/SantoshAT15 fatcat:7mcr2gbmrjbyxnlwofhlhzdppy

Quantification of Uncertainties in Biomedical Image Quantification [article]

Bjoern Menze, Leo Joskowicz, Spyridon Bakas, Andras Jakab, Ender Konukoglu, Anton Becker, Christoph Berger
2020 Zenodo  
This is the challenge design document for the "Quantification of Uncertainties in Biomedical Image Quantification" Challenge, accepted for MICCAI 2020.  ...  directly inferred from human expert annotations.  ...  Examples: Training and test cases both represent a CT image of a human brain.  ... 
doi:10.5281/zenodo.3718911 fatcat:snq6j3z2ozgpvkxkxxtsawiktu

HEad and neCK TumOR segmentation and outcome prediction in PET/CT images [article]

Vincent Andrearczyk, Valentin Oreiller, Martin Vallières, Mathieu Hatt, Catherine Cheze-Le Rest, Dimitris Visvikis, Mario Jreige, Hesham Elhalawani, Sarah Boughdad, John O. Prior, Adrien Depeursinge
2021 Zenodo  
Methods for automated lesion segmentation in medical images were proposed in various contexts, often achieving expert-level performance (Heimann and Meinzer 2009), (Menze et al. 2015).  ...  Surprisingly few studies evaluated the performance of computerized automated segmentation of tumor lesions in PET and CT images (Song et al. 2013),(Blanc-Durand et al. 2018), (Moe et al. 2019).  ...  . • Segmentation outputs will be submitted by the participating teams via AIcrowd.  ... 
doi:10.5281/zenodo.4573154 fatcat:tz6uj3ef2ncz7emesdhqy5ivhm
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