Deep Neural Architectures for Medical Image Semantic Segmentation: Review

Khan, Muhammad Zubair; Gajendran, Mohan Kumar; Lee, Yugyung; Khan, Muazzam A.

doi:10.1109/access.2021.3086530

Cited by 63 publications

(25 citation statements)

References 174 publications

(155 reference statements)

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“…Recent denoising methods are mostly based on supervised CNNs trained to recover ground truth segmentations from artificially corrupted versions of the same maps [23,24,27]. However, the employed corruption strategies are often handcrafted (random erosion and dilation, swapping of labels, etc.…”

Section: Training Scheme For the Denoising Modulementioning

confidence: 99%

Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining

Billot¹,

Colin²,

Arnold³

et al. 2022

Preprint

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Retrospective analysis of brain MRI scans acquired in the clinic has the potential to enable neuroimaging studies with sample sizes much larger than those found in research datasets. However, analysing such clinical images "in the wild" is challenging, since subjects are scanned with highly variable protocols (MR contrast, resolution, orientation, etc.). Nevertheless, recent advances in convolutional neural networks (CNNs) and domain randomisation for image segmentation, best represented by the publicly available method SynthSeg, may enable morphometry of clinical MRI at scale. In this work, we first evaluate SynthSeg on an uncurated, heterogeneous dataset of more than 10,000 scans acquired at Massachusetts General Hospital. We show that SynthSeg is generally robust, but frequently falters on scans with low signal-to-noise ratio or poor tissue contrast. Next, we propose SynthSeg + , a novel method that greatly mitigates these problems using a hierarchy of conditional segmentation and denoising CNNs. We show that this method is considerably more robust than SynthSeg, while also outperforming cascaded networks and state-of-the-art segmentation denoising methods. Finally, we apply our approach to a proof-of-concept volumetric study of ageing, where it closely replicates atrophy patterns observed in research studies conducted on high-quality, 1 mm, T1-weighted scans. The code and trained model are publicly available at https://github.com/BBillot/SynthSeg.

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Section: Training Scheme For the Denoising Modulementioning

confidence: 99%

Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining

Billot¹,

Colin²,

Arnold³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, there has been a surge of interest in machine learning as significant advancements in computational hardware (Shi et al, 2016 ) facilitate the development of novel machine learning approaches as solutions to problems in various disciplines from financial forecasting to public transportation and healthcare (Trafalis and Ince, 2000 ; Omrani, 2015 ; Ahmad et al, 2018 ). There are several predictive techniques in machine learning with various complexities, ranging from simple linear models to advanced non-linear models such as those based on deep learning algorithms (Shailaja et al, 2018 ; Khan et al, 2021 ; Saxe et al, 2021 ). Currently, available ERG analysis methods, such as those developed by Hood et al ( 2000 ); Ventura and Porciatti ( 2006 ), have contributed to a significantly improved understanding of the relationship between ERG signals and vision loss.…”

Section: Introductionmentioning

confidence: 99%

Novel Machine-Learning Based Framework Using Electroretinography Data for the Detection of Early-Stage Glaucoma

et al. 2022

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PurposeEarly-stage glaucoma diagnosis has been a challenging problem in ophthalmology. The current state-of-the-art glaucoma diagnosis techniques do not completely leverage the functional measures' such as electroretinogram's immense potential; instead, focus is on structural measures like optical coherence tomography. The current study aims to take a foundational step toward the development of a novel and reliable predictive framework for early detection of glaucoma using machine-learning-based algorithm capable of leveraging medically relevant information that ERG signals contain.MethodsERG signals from 60 eyes of DBA/2 mice were grouped for binary classification based on age. The signals were also grouped based on intraocular pressure (IOP) for multiclass classification. Statistical and wavelet-based features were engineered and extracted. Important predictors (ERG tests and features) were determined, and the performance of five machine learning-based methods were evaluated.ResultsRandom forest (bagged trees) ensemble classifier provided the best performance in both binary and multiclass classification of ERG signals. An accuracy of 91.7 and 80% was achieved for binary and multiclass classification, respectively, suggesting that machine-learning-based models can detect subtle changes in ERG signals if trained using advanced features such as those based on wavelet analyses.ConclusionsThe present study describes a novel, machine-learning-based method to analyze ERG signals providing additional information that may be used to detect early-stage glaucoma. Based on promising performance metrics obtained using the proposed machine-learning-based framework leveraging an established ERG data set, we conclude that the novel framework allows for detection of functional deficits of early/various stages of glaucoma in mice.

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“…In order to solve these difficulties, most early image semantic segmentation technologies are based on traditional methods, mainly including segmentation methods based on the threshold, edge detection, and region [ 9 ]. With the emergence of deep learning, the image semantic segmentation method based on deep learning gradually replaces the traditional methods, and its accuracy, speed, and other performance indicators have been greatly improved [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Image Semantic Segmentation Method Based on Deep Fusion Network and Conditional Random Field

Wang

Yang

2022

Computational Intelligence and Neuroscience

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Aiming at the problems of missing points and wrong points in image semantic segmentation under complex background and small target, an image semantic segmentation method based on the fully convolution neural network and conditional random field is proposed. First, the deconvolution fusion structure is added to the fully convolution neural network to build a deep fusion network. The multiscale features are automatically obtained through the deep fusion network, and the shallow detail information and deep semantic information are fused to improve the processing accuracy of image rough segmentation. Then, the bivariate potential function of the conditional random field is optimized based on the convolution neural network, and it is used for image fine segmentation to obtain the final image segmentation result. Finally, the proposed method is experimentally analyzed based on the Cityscapes dataset. The results show that the proposed method can achieve accurate image segmentation, and the area under the segmentation curve of the overall size target is 93.6%, which is better than other methods.

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Deep Neural Architectures for Medical Image Semantic Segmentation: Review

Cited by 63 publications

References 174 publications

Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining

Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining

Novel Machine-Learning Based Framework Using Electroretinography Data for the Detection of Early-Stage Glaucoma

Image Semantic Segmentation Method Based on Deep Fusion Network and Conditional Random Field

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