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Biomedical Image Segmentation via Representative Annotation
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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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]
2017
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2021
Zenodo
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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]
2018
arXiv
pre-print
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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
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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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]
2022
arXiv
pre-print
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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
2012
Journal of Biomedical Informatics
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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]
2020
Zenodo
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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]
2021
Zenodo
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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]
2021
Zenodo
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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
2014
Journal of Biomedical Semantics
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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
2015
2015 IEEE 28th International Symposium on Computer-Based Medical Systems
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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]
2020
Zenodo
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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]
2021
Zenodo
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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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