3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Özgün Çiçek,Ahmed Abdulkadir,Ahmed Abdulkadir,Soeren S. Lienkamp,Thomas Brox,Olaf Ronneberger,Olaf Ronneberger +6 more
- pp 424-432
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TLDR
In this paper, the authors propose a network for volumetric segmentation that learns from sparsely annotated volumetrized images, which is trained end-to-end from scratch, i.e., no pre-trained network is required.Abstract:
This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automated setup, the user annotates some slices in the volume to be segmented. The network learns from these sparse annotations and provides a dense 3D segmentation. (2) In a fully-automated setup, we assume that a representative, sparsely annotated training set exists. Trained on this data set, the network densely segments new volumetric images. The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts. The implementation performs on-the-fly elastic deformations for efficient data augmentation during training. It is trained end-to-end from scratch, i.e., no pre-trained network is required. We test the performance of the proposed method on a complex, highly variable 3D structure, the Xenopus kidney, and achieve good results for both use cases.read more
Citations
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Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
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nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Fabian Isensee,Fabian Isensee,Paul F. Jaeger,Simon A. A. Kohl,Jens Petersen,Jens Petersen,Klaus H. Maier-Hein,Klaus H. Maier-Hein +7 more
TL;DR: nnU-Net as mentioned in this paper is a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task.
Journal ArticleDOI
Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw,Joseph R. Ledsam,Bernardino Romera-Paredes,Stanislav Nikolov,Nenad Tomasev,Sam Blackwell,Harry Askham,Xavier Glorot,Brendan O'Donoghue,Daniel Visentin,George van den Driessche,Balaji Lakshminarayanan,Clemens Meyer,Faith Mackinder,Simon Bouton,Kareem Ayoub,Reena Chopra,Dominic King,Alan Karthikesalingam,Cian Hughes,Rosalind Raine,Julian Hughes,Dawn A Sim,Catherine A Egan,Adnan Tufail,Hugh Montgomery,Demis Hassabis,Geraint Rees,Trevor Back,Peng T. Khaw,Mustafa Suleyman,Julien Cornebise,Pearse A. Keane,Olaf Ronneberger +33 more
TL;DR: A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease.
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Proceedings ArticleDOI
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References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
Proceedings ArticleDOI
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Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Posted Content
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Batch Normalization as mentioned in this paper normalizes layer inputs for each training mini-batch to reduce the internal covariate shift in deep neural networks, and achieves state-of-the-art performance on ImageNet.