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Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation [chapter]

Zhuo Zhao, Lin Yang, Hao Zheng, Ian H. Guldner, Siyuan Zhang, Danny Z. Chen
2018 Lecture Notes in Computer Science  
In this paper, we propose a new weak annotation approach for training a fast deep learning 3D instance segmentation model without using full voxel mask annotation.  ...  Instance segmentation in 3D images is a fundamental task in biomedical image analysis.  ...  To train a fast 3D instance segmentation model without high 3D annotation effort, in this paper, we present an end-to-end deep learning 3D instance segmentation model utilizing weak annotation.  ... 
doi:10.1007/978-3-030-00937-3_41 fatcat:iumls34wbrernpky4g2u3bhm7q

Learning to segment microscopy images with lazy labels [article]

Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Peter Schuetz, Carola-Bibiane Schönlieb
2020 arXiv   pre-print
In this paper, we introduce a deep convolutional neural network for microscopy image segmentation.  ...  The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for segmenting bioimages that can contain numerous object instances with thin separations  ...  Segmentation of microscopy and biomedical images. Various multi-task deep learning methods have been developed for processing microscopy images and biomedical images.  ... 
arXiv:1906.12177v2 fatcat:omiyieb72zb5hatjojjodpi7ta

A Survey on Deep Learning of Small Sample in Biomedical Image Analysis [article]

Pengyi Zhang, Yunxin Zhong, Yulin Deng, Xiaoying Tang, Xiaoqiong Li
2019 arXiv   pre-print
However, in many cases of biomedical image analysis, deep learning techniques suffer from the small sample learning (SSL) dilemma caused mainly by lack of annotations.  ...  In order to accelerate the clinical usage of biomedical image analysis based on deep learning techniques, we intentionally expand this survey to include the explanation methods for deep models that are  ...  Acknowledgements The authors would like to thank members of the Medical Image Analysis for discussions and suggestions.  ... 
arXiv:1908.00473v1 fatcat:atotvdxp6janve2mz3swyf47xa

Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation

Vien Ngoc Dang, Francesco Galati, Rosa Cortese, Giuseppe Di Giacomo, Viola Marconetto, Prateek Mathur, Karim Lekadir, Marco Lorenzi, Ferran Prados, Maria A. Zuluaga
2021 Medical Image Analysis  
Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors.  ...  The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by ∼77% w.r.t. learning-based segmentation methods using pixel-wise labels for training.  ...  FP is funded in part by the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact), and by a Non-Clinical Postdoctoral Guarantors  ... 
doi:10.1016/j.media.2021.102263 pmid:34731770 fatcat:s3crgql6xvaljbbt7hggb32n5e

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models [article]

Jialin Peng, Ye Wang
2021 arXiv   pre-print
application of deep learning models in medical image segmentation.  ...  The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  For 3D medical image segmentation, uniformly sampled slices with annotations were used in [220] - [224] to train a 3D deep network model by assigning a zero weight to unannotated voxels in the loss  ... 
arXiv:2103.00429v1 fatcat:p44a5e34sre4nasea5kjvva55e

Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation [article]

Vien Ngoc Dang and Francesco Galati and Rosa Cortese and Giuseppe Di Giacomo and Viola Marconetto and Prateek Mathur and Karim Lekadir and Marco Lorenzi and Ferran Prados and Maria A. Zuluaga
2021 arXiv   pre-print
Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors.  ...  The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by ~77% w.r.t. learning-based segmentation methods using pixel-wise labels for training.  ...  , This work presents a novel framework to address the challenges faced by deep learning-based 3D vessel segmentation.  ... 
arXiv:2101.09321v4 fatcat:dji4j2su2rhftnl3ajlihgi2qm

Point-supervised Segmentation of Microscopy Images and Volumes via Objectness Regularization [article]

Shijie Li, Neel Dey, Katharina Bermond, Leon von der Emde, Christine A. Curcio, Thomas Ach, Guido Gerig
2021 arXiv   pre-print
Annotation is a major hurdle in the semantic segmentation of microscopy images and volumes due to its prerequisite expertise and effort.  ...  This work enables the training of semantic segmentation networks on images with only a single point for training per instance, an extreme case of weak supervision which drastically reduces the burden of  ...  However, the construction of training sets with the scale required for deep learning-based image segmentation is an often insurmountable investment of time and resources for some study designs.  ... 
arXiv:2103.05617v2 fatcat:h3phricp6jfqrcynsqxf2e4vui

Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation [article]

K. Ruwani M. Fernando, Chris P. Tsokos
2021 arXiv   pre-print
In this study, we critically review major statistical and deep learning models and their applications in brain imaging research with a focus on MRI-based brain tumor segmentation.  ...  Driven by the breakthroughs in computer vision, deep learning became the de facto standard in the domain of medical imaging.  ...  and deep learning voxel-based segmentation.  ... 
arXiv:2103.05529v1 fatcat:iqu5ix5tgre6pnokdmoejywh74

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Jialin Peng, Ye Wang
2021 IEEE Access  
application of deep learning models in medical image segmentation.  ...  INDEX TERMS Medical image segmentation, semi-supervised segmentation, partially-supervised segmentation, noisy label, sparse annotation. 36828  ...  VOLUME SEGMENTATION WITH SPARSELY ANNOTATED SLICES For 3D medical image segmentation, uniformly sampled slices with annotations were used in [220] - [224] to train a 3D deep network model by assigning  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

Deep Learning for Automated Medical Image Analysis [article]

Wentao Zhu
2019 arXiv   pre-print
Recent advances in deep learning enable us to rethink the ways of clinician diagnosis based on medical images.  ...  Second, we will demonstrate how to use the weakly labeled data for the mammogram breast cancer diagnosis by efficiently design deep learning for multi-instance learning.  ...  Recent advances in deep learning enable us to rethink the ways of clinician diagnosis based on medical images.  ... 
arXiv:1903.04711v1 fatcat:xigyugddlrentc42o5mnlbhdkq

Boosting Multilabel Semantic Segmentation for Somata and Vessels in Mouse Brain

Xinglong Wu, Yuhang Tao, Guangzhi He, Dun Liu, Meiling Fan, Shuo Yang, Hui Gong, Rong Xiao, Shangbin Chen, Jin Huang
2021 Frontiers in Neuroscience  
Deep convolutional neural networks (DCNNs) are widely utilized for the semantic segmentation of dense nerve tissues from light and electron microscopy (EM) image data; the goal of this technique is to  ...  ; this framework eventually resulted in improved segmentation task performance.  ...  We thank the Optical Bioimaging Core Facility of WNLO-HUST for the support in data acquisition.  ... 
doi:10.3389/fnins.2021.610122 pmid:33912000 pmcid:PMC8071950 fatcat:ej325nac7zf3hpm53klwvxahfq

Assessment of deep learning algorithms for 3D instance segmentation of confocal image datasets [article]

Anuradha Kar, Manuel Petit, Yassin Refahi, Guillaume Cerutti, Christophe Godin, Jan Traas
2021 bioRxiv   pre-print
Recently, deep learning (DL) pipelines have been developed which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state-of-the-art for image segmentation  ...  The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artefacts.  ...  instance segmentation in 3D biomedical images using weak annotation. In A. F. Frangi, J. A. Schnabel, C. Davatzikos, C. Alberola-López, & G.  ... 
doi:10.1101/2021.06.09.447748 fatcat:ot56o5uccfgrvdubnacezre5wq

Evaluation of Deep Learning architectures for complex immunofluorescence nuclear image segmentation

Florian Kromp, Lukas Fischer, Eva Bozsaky, Inge M. Ambros, Wolfgang Dorr, Klaus Beiske, Peter F. Ambros, Allan Hanbury, Sabine Taschner-Mandl
2021 IEEE Transactions on Medical Imaging  
Deep learning based segmentation requires annotated datasets for training, but annotated fluorescence nuclear image datasets are rare and of limited size and complexity.  ...  In this work, we evaluate and compare the segmentation effectiveness of multiple deep learning architectures (U-Net, U-Net ResNet, Cellpose, Mask R-CNN, KG instance segmentation) and two conventional algorithms  ...  [23] use the SegNet architecture [24] to segment 3D image stacks of rat kidney tissue.  ... 
doi:10.1109/tmi.2021.3069558 pmid:33784615 fatcat:65ujfuqczfbn7etu7rh6i5w6nq

Dental pathology detection in 3D cone-beam CT [article]

Adel Zakirov, Matvey Ezhov, Maxim Gusarev, Vladimir Alexandrovsky, Evgeny Shumilov
2018 arXiv   pre-print
Our task is two-fold: a) find locations of each present tooth inside a 3D image volume, and b) detect several common tooth conditions in each tooth.  ...  Cone-beam computed tomography (CBCT) is a valuable imaging method in dental diagnostics that provides information not available in traditional 2D imaging.  ...  and deep learning techniques in dental image analysis.  ... 
arXiv:1810.10309v1 fatcat:ys6n7ziggrdlhds2necfqislje

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation [article]

Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, Xiaowei Ding
2020 arXiv   pre-print
The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks.  ...  data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations.  ...  Cheplygina et al. (2019) reviewed semi-supervised, multi-instance learning, and transfer learning in medical image analysis, covering both deep learning and traditional segmentation methods.  ... 
arXiv:1908.10454v2 fatcat:mjvfbhx75bdkbheysq3r7wmhdi
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