Test-time adaptable neural networks for robust medical image segmentation

Karani, Neerav; Erdil, Ertunç; Chaitanya, Krishna; Konukoğlu, Ender

doi:10.1016/j.media.2020.101907

Cited by 128 publications

(113 citation statements)

References 37 publications

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“…If an image has one or more contours associated with it, the same transformation is applied to the contours. Geometric transformations are so common that they were utilised by 92 of the 93 basic augmentation studies 15–106 …”

Section: Methodsmentioning

confidence: 99%

“…Gamma correction (3j), 107 linear contrast (3k) and histogram equalisation (3l) are common methods to adjust the contrast of an image. Twenty‐eight studies utilised intensity operations for data augmentation 18,20,21,28,34,38,40–42,45,46,55,62,67,70,75–77,79,84,87,88,94,102–104,106,108 …”

Section: Methodsmentioning

confidence: 99%

“…Uniform noise, which modifies values by randomly sampling a uniform distribution (3n) or salt and pepper noise, where pixels are randomly set to black or white (3o) have also be used. 25 studies injected noise for data augmentation 24,25,27,30,31,36,39,51,55,57,67,73,75,81,83,87,88,91,93,99,100,103–106 …”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A review of medical image data augmentation techniques for deep learning applications

et al. 2021

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Summary Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning‐based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren’t typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state‐of‐the‐art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A review of medical image data augmentation techniques for deep learning applications

et al. 2021

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“…Till date, several papers have been published on this type of CNN architecture for biomedical 30 , 31 and areal applications 32 , 33 . Moreover, this type of architectures are constantly improving by cascading or fusing the CNNs in biomedical 37 , 38 and remote sensing applications 39 .…”

Section: Introductionmentioning

confidence: 99%

Fully-automated root image analysis (faRIA)

Narisetti

Henke

Seiler

et al. 2021

Sci Rep

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High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.

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“…Such pixels combine into sets of segments, and neighboring image segments will differ in the same characteristics. Due to new emerging opportunities, artifi-*tatarkanov@ikti.ru cial NNs have become in high demand in recent years to analyze complex structured images resulting from medical imaging [15][16][17][18]42]. A prime example of a successful application of NNs is the RatLesNetv2 model [19].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of neural network structure for the analysis of complex structured ocular fundus images

Tatarkanov¹,

Alexandrov²,

Glashev³

2021

J Appl Eng Science

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This paper proposes an algorithm for synthesizing a neural network (NN) structure to analyze complex structured, low entropy, ocular fundus images, characterized by iterative tuning of the adaptive model’s solver modules. This algorithm will assist in synthesizing models of NNs that meet the predetermined characteristics of the classification quality. The relevance of automating the process of ocular diagnostics of fundus pathologies is due to the need to develop domestic medical decision-making systems. Because of using the developed algorithm, the NN structure is synthesized, which will include two solver modules, and is intended to classify the dual-alternative information. Automated hybrid NN structures for intelligent segmentation of complex structured, low entropy, retinal images should provide increased efficiency of ocular diagnostics of fundus pathologies, reduce the burden on specialists, and decrease the negative impact of the human factor in diagnosis.

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Test-time adaptable neural networks for robust medical image segmentation

Cited by 128 publications

References 37 publications

A review of medical image data augmentation techniques for deep learning applications

A review of medical image data augmentation techniques for deep learning applications

Fully-automated root image analysis (faRIA)

Synthesis of neural network structure for the analysis of complex structured ocular fundus images

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