Image Segmentation Using Deep Learning: A Survey

Minaee, Shervin; Boykov, Yuri; Porikli, Fatih; Plaza, Antonio; Kehtarnavaz, Nasser; Terzopoulos, Demetri

doi:10.1109/tpami.2021.3059968

Cited by 1,635 publications

(878 citation statements)

References 197 publications

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“…One key benefit of DL is that features are automatically learned from the input data, thereby negating the need for laborious manual feature extraction, and allow well-generalised models to be trained using datasets from diverse imaging environments. A common DL architecture is deep convolutional neural networks (CNNs), which have delivered state-of-the-art (SOTA) performance for computer vision tasks such as image classification/regression, object detection, and image segmentation [7][8][9]. The progress of transfer learning, a technique that allows the use of pretrained SOTA CNNs as base models in DL, and the availability of public DL libraries have contributed to the exponential adoption of DL in plant phenotyping.…”

Section: Introductionmentioning

confidence: 99%

Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping

2021

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Automated machine learning (AutoML) has been heralded as the next wave in artificial intelligence with its promise to deliver high-performance end-to-end machine learning pipelines with minimal effort from the user. However, despite AutoML showing great promise for computer vision tasks, to the best of our knowledge, no study has used AutoML for image-based plant phenotyping. To address this gap in knowledge, we examined the application of AutoML for image-based plant phenotyping using wheat lodging assessment with unmanned aerial vehicle (UAV) imagery as an example. The performance of an open-source AutoML framework, AutoKeras, in image classification and regression tasks was compared to transfer learning using modern convolutional neural network (CNN) architectures. For image classification, which classified plot images as lodged or non-lodged, transfer learning with Xception and DenseNet-201 achieved the best classification accuracy of 93.2%, whereas AutoKeras had a 92.4% accuracy. For image regression, which predicted lodging scores from plot images, transfer learning with DenseNet-201 had the best performance (R2 = 0.8303, root mean-squared error (RMSE) = 9.55, mean absolute error (MAE) = 7.03, mean absolute percentage error (MAPE) = 12.54%), followed closely by AutoKeras (R2 = 0.8273, RMSE = 10.65, MAE = 8.24, MAPE = 13.87%). In both tasks, AutoKeras models had up to 40-fold faster inference times compared to the pretrained CNNs. AutoML has significant potential to enhance plant phenotyping capabilities applicable in crop breeding and precision agriculture.

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Section: Introductionmentioning

confidence: 99%

Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping

2021

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“…In contrast, other reviews discussed deep segmentation architectures for specific medical application. The surveys [42], [46], [47] highlighted major deep networks and training strategies for medical image analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [47] have provided a comprehensive review of deep learning-based image segmentation architectures used for general computer vision tasks. This survey includes medical image segmentation architectures; however, it primarily focused on object detection and segmentation of general image datasets.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

et al. 2021

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Deep learning has an enormous impact on medical image analysis. Many computer-aided diagnostic systems equipped with deep networks are rapidly reducing human intervention in healthcare. Among several applications, medical image semantic segmentation is one of the core areas of active research to delineate the anatomical structures and other regions of interest. It has a significant contribution to healthcare and provides guided interventions, radiotherapy, and improved radiological diagnostics. The underlying article provides a brief overview of deep convolutional neural architecture, the platforms and applications of deep neural networks, metrics used for empirical evaluation, state-of-the-art semantic segmentation architectures based on a foundational convolution concept, and a review of publicly available medical image datasets highlighting four distinct regions of interest. The article also analyzes the existing work and provides open-ended potential research directions in deep medical image semantic segmentation.

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“…The survey [5] covers the recent literature in deeplearning-based image segmentation, including more than 100 relevant segmentation methods proposed to date. In the following part, representative semantic segmentation methods will be introduced, which are mentioned in the survey [5].…”

Section: Introductionmentioning

confidence: 99%

A Serial-Parallel Self-Attention Network Joint With Multi-Scale Dilated Convolution

et al. 2021

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Semantic segmentation is a high-level task in the field of computer vision, which paves the way for the realization of a complete understanding of the scene, and has been widely used in automatic driving, human-computer interaction, virtual reality, and other aspects. Recently, the semantic segmentation method of convolutional neural networks with deep structure has been more accurate and efficient than other methods. However, there are some problems in these methods, such as the loss of information caused by the down-sampling operation, the lack of usage of image context information, and the neglect of the relationship between spatial features and channel features. To solve these problems, a novel self-attention network based on the series-parallel structure is proposed in the paper. Firstly, a multi-scale dilated convolution backbone network is constructed by combining the dilated convolution and the residual network, which makes up for the information loss caused by the restriction of the receptor field in the ordinary network and improves the richness of extracted features. Secondly, the self-attention modules are stacked with serial and parallel structures, which can effectively extract the contextual information of space, channel, and space-channel and fully integrate them. Finally, the proposed algorithm is tested extensively and compared with the existing classical algorithms. The experimental results show that the proposed algorithm achieves state-of-the-art performance on the public dataset.

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Image Segmentation Using Deep Learning: A Survey

Cited by 1,635 publications

References 197 publications

Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping

Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

A Serial-Parallel Self-Attention Network Joint With Multi-Scale Dilated Convolution

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