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Empirical comparison of cell segmentation algorithms using an annotated dataset

P. Bamford
Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)  
Three popular algorithms (Viterbi active contour, watershed and a thresholding-based method) for segmenting cell nuclei are compared empirically.  ...  This is achieved via a large (20,000) image dataset that has been annotated by three independent non-expert observers.  ...  CONCLUSIONS We have considered methodologies for evaluating cell segmentation using annotated examples.  ... 
doi:10.1109/icip.2003.1246871 dblp:conf/icip/Bamford03 fatcat:rya5o2v4xravdbsl7hfp3dj3qq

Dense cellular segmentation for EM using 2D–3D neural network ensembles

Matthew D. Guay, Zeyad A. S. Emam, Adam B. Anderson, Maria A. Aronova, Irina D. Pokrovskaya, Brian Storrie, Richard D. Leapman
2021 Scientific Reports  
We present an algorithm using novel hybrid 2D–3D segmentation networks to produce dense cellular segmentations with accuracy levels that outperform baseline methods and approach those of human annotators  ...  Here, we define dense cellular segmentation as a multiclass semantic segmentation task for modeling cells and large numbers of their organelles, and give an example in human blood platelets.  ...  This work also used the computational resources of the NIH HPC Biowulf cluster. (https ://  ... 
doi:10.1038/s41598-021-81590-0 pmid:33510185 pmcid:PMC7844272 fatcat:4hxhcwt2lrefxeddi2libml7mq

Dense cellular segmentation using 2D-3D neural network ensembles for electron microscopy [article]

Matthew Guay, Zeyad Emam, Adam Anderson, Maria Aronova, Richard D Leapman
2020 bioRxiv   pre-print
We describe how to use ensembles of 2D-3D neural networks to compute dense cellular segmentations of cells and organelles inside two human platelet tissue samples.  ...  We conclude by discussing ongoing challenges for realizing practical dense cellular segmentation algorithms.  ...  (g) Annotator comparison (AC) dataset orthoslice, segmented cell highlighted. (h-j) Annotator comparison cell segmentations, comparing the two human annotators and our best algorithm.  ... 
doi:10.1101/2020.01.05.895003 fatcat:kridj7csu5f43o3kt6irkwpv74

Cell Segmentation of 2D Phase-Contrast Microscopy Images with Deep Learning Method

Aydin Ayanzadeh, Huseyin Onur Yagar, Ozden Yalcin Ozuysal, Devrim Pesen Okvur, Behcet Ugur Toreyin, Devrim Unay, Sevgi Onal
2019 2019 Medical Technologies Congress (TIPTEKNO)  
Experimental results suggest that the proposed model provides superior performance in comparison to traditional state-of-the-art segmentation algorithms.  ...  Moreover, we applied multi-combination augmentation to our dataset which has increased the performance of segmentation accuracy significantly.  ...  ACKNOWLEDGMENT The data used in this study is collected under the Marie Curie IRG grant (no: FP7 PIRG08-GA-2010-27697).  ... 
doi:10.1109/tiptekno.2019.8894978 fatcat:uy4tl5z745cirmjeqlcngq35du

CNN Based Yeast Cell Segmentation in Multi-modal Fluorescent Microscopy Data

Ali Selman Aydin, Abhinandan Dubey, Daniel Dovrat, Amir Aharoni, Roy Shilkrot
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
cell-segmentation methods) fail.  ...  Our model is capable of segmenting patches extracted from yeast live-cell experiments with a mIOU score of 0.71 on unseen patches drawn from independent experiments.  ...  Overall, the dataset we have created poses a challenging scenario for a cell segmentation algorithm.  ... 
doi:10.1109/cvprw.2017.105 dblp:conf/cvpr/AydinDDAS17 fatcat:6pxhxmqtrbdhhicchfhg6csu7e

An Interpretable Algorithm for Uveal Melanoma Subtyping from Whole Slide Cytology Images [article]

Haomin Chen, T.Y. Alvin Liu, Catalina Gomez, Zelia Correa, Mathias Unberath
2021 arXiv   pre-print
Our method embeds every automatically segmented cell of a candidate cytology image as a point in a 2D manifold defined by many representative slides, which enables reasoning about the cell-level composition  ...  On our in house cytology dataset of 88 uveal melanoma patients, the proposed method achieves an accuracy of 87.5% that compares favorably to all competing approaches, including deep "black box" models.  ...  using an interpretable rule-based algorithm.  ... 
arXiv:2108.06246v1 fatcat:hec3dxbwjvg5zobn5pq6jp6j4y

SAGE: An approach and implementation empowering quick and reliable quantitative analysis of segmentation quality

Danna Gurari, Suele Ki Kim, Eugene Yang, Brett Isenberg, Tuan A. Pham, Alberto Purwada, Patricia Solski, Matthew Walker, Joyce Y. Wong, Margrit Betke
2013 2013 IEEE Workshop on Applications of Computer Vision (WACV)  
Finally, three studies are presented to highlight the impact of annotation tools, annotators, and fusion methods on establishing trusted gold standard segmentations for cell and artery images.  ...  Finding the outline of an object in an image is a fundamental step in many vision-based applications.  ...  Acknowledgments The authors thank Netta Gurari and Diane Theriault for their useful discussions and gratefully acknowledge funding from the National Science Foundation (IIS-0910908).  ... 
doi:10.1109/wacv.2013.6475057 dblp:conf/wacv/GurariKYIPPSWWB13 fatcat:fgdjdbezjzbxtbykcs2h75j4e4

DETCID: Detection of Elongated Touching Cells with Inhomogeneous Illumination using a Deep Adversarial Network [article]

Ali Memariani, Ioannis A. Kakadiaris
2020 arXiv   pre-print
Detection of C. diff cells in scanning electron microscopy (SEM) images is an important task to quantify the efficacy of the under-development treatments.  ...  In this paper, DETCID, a deep cell detection method using adversarial training, specifically robust to inhomogeneous illumination and occlusion, is proposed.  ...  [16] performed an objective comparison of many shallow cell detection algorithms with deep convolutional networks.  ... 
arXiv:2007.06716v1 fatcat:ckhrjmg6dnf53p2krrirllvp54

YouTube-VOS: Sequence-to-Sequence Video Object Segmentation [chapter]

Ning Xu, Linjie Yang, Yuchen Fan, Jianchao Yang, Dingcheng Yue, Yuchen Liang, Brian Price, Scott Cohen, Thomas Huang
2018 Lecture Notes in Computer Science  
End-to-end sequential learning to explore spatialtemporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i.e., even the largest video segmentation  ...  Experiments show that the large scale dataset is indeed a key factor to the success of our model.  ...  We find empirically that our dataset is effective in training different segmentation algorithm.  ... 
doi:10.1007/978-3-030-01228-1_36 fatcat:jxbeuhclmjgvzosjkyi43r7goq

YouTube-VOS: Sequence-to-Sequence Video Object Segmentation [article]

Ning Xu, Linjie Yang, Yuchen Fan, Jianchao Yang, Dingcheng Yue, Yuchen Liang, Brian Price, Scott Cohen, Thomas Huang
2018 arXiv   pre-print
End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i.e., even the largest video segmentation  ...  Experiments show that the large scale dataset is indeed a key factor to the success of our model.  ...  We find empirically that our dataset is effective in training different segmentation algorithm.  ... 
arXiv:1809.00461v1 fatcat:ufu4eo2mlrakplypne5njogkpm

Signal Quality Index: a novel algorithm for quantitative assessment of functional near infrared spectroscopy signal quality

María Sofía Sappia, Naser Hakimi, Willy Colier, Jörn Horschig
2020 Biomedical Optics Express  
The results on a dataset annotated by independent fNIRS experts showed SQI performed significantly better (p<0.05) than PHOEBE (placing headgear optodes efficiently before experimentation) and SCI (scalp  ...  Employment of the proposed algorithm to estimate the signal quality before processing the fNIRS signals increases certainty in the interpretations.  ...  Roemer van der Meij and Liucija Svinkunaite for their contribution to an early version of the algorithm, as well as the fNIRS experts working at Artinis Medical Systems B.V., who participated in annotating  ... 
doi:10.1364/boe.409317 pmid:33282521 pmcid:PMC7687963 fatcat:62yolcvwsnawnoxs35j5yy3yhy

VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning [article]

Mengyang Zhao, Quan Liu, Aadarsh Jha, Ruining Deng, Tianyuan Yao, Anita Mahadevan-Jansen, Matthew J.Tyska, Bryan A. Millis, Yuankai Huo
2021 arXiv   pre-print
The performance is also competitive on two sparsely annotated cohorts with 20.6% and 2% of data-set having segmentation annotations.  ...  We evaluate our VoxelEmbed method on four 3D datasets (with different cell types) from the ISBI Cell Tracking Challenge.  ...  The 3D ISBI Cell Tracking Challenge dataset, (a) Complete and (b) Sparse manual annotations, as well as the VoxelEmbed results are presented.  ... 
arXiv:2106.11480v1 fatcat:crnya3pi6jgttavf7fm7l33z34

Automatic Label Correction for the Accurate Edge Detection of Overlapping Cervical Cells [article]

Jiawei Liu, Qiang Wang, Huijie Fan, Shuai Wang, Wentao Li, Yandong Tang, Danbo Wang, Mingyi Zhou, Li Chen
2021 arXiv   pre-print
Using the proposed algorithm, we constructed an open cervical cell edge detection dataset (CCEDD) with high labeling accuracy.  ...  In this paper, to accurately segment images of multiple overlapping cervical cells with deep learning models, we propose an automatic label correction algorithm to improve the edge positioning accuracy  ...  An image of BSD500 [39] is annotated by six persons, while an image in our dataset is annotated by one person.  ... 
arXiv:2010.01919v2 fatcat:zy574eiacvceti2pzrbhurcl6y

SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

Tasleem Kausar, Wang MingJiang, M. Adnan Ashraf, Adeeba Kausar
2020 IEEE Access  
SmallMitosis framework consists of an atrous fully convolution based segmentation (A-FCN) model and a deep multiscale (MS-RCNN) detector.  ...  Using these estimated bounding box annotations, MS-RCNN detector is trained to detect small size mitosis from weakly labeled datasets.  ...  In [11] , fully annotated mitosis dataset is used for mitosis localization and classification while a weakly annotated dataset is used for segmentation and estimation of mitosis mask labels.  ... 
doi:10.1109/access.2020.3044625 fatcat:2wg4gi346rbq7lepur5xrixwqe

Joint Cell Nuclei Detection and Segmentation in Microscopy Images Using 3D Convolutional Networks [article]

Sundaresh Ram and Vicky T. Nguyen and Kirsten H. Limesand and Mert R. Sabuncu
2018 arXiv   pre-print
In our experiments, we conduct a thorough evaluation of both detection accuracy and segmentation quality, on two different datasets.  ...  The results show that the proposed method provides significantly improved detection and segmentation accuracy compared to state-of-the-art and benchmark algorithms.  ...  [1] propose a cell detection and segmentation algorithm that uses an adaptive multi-scale Laplacian-of-Gaussian (LoG) filtering for detecting cells, and graph-cut optimization to segment each detected  ... 
arXiv:1805.02850v2 fatcat:epqfffmknza7fndjfisdvegm5i
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