Deep learning‐based panoptic segmentation: Recent advances and perspectives

Chuang, Yuelong; Zhang, Shiqing; Zhao, Xiaoming

doi:10.1049/ipr2.12853

Cited by 7 publications

(4 citation statements)

References 137 publications

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“…Existing image-segmentation methods include traditional segmentation methods [ 7 , 8 ] and deep learning segmentation methods [ 9 , 10 ]. Although deep learning segmentation methods are more efficient and intelligent, they require a large amount of data for training to improve the generalization ability.…”

Section: Introductionmentioning

confidence: 99%

A Convexity-Preserving Level-Set Method for the Segmentation of Tumor Organoids

Lei,

Gui,

Liu

2024

Bioengineering

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Tumor organoid cultures play a crucial role in clinical practice, particularly in guiding medication by accurately determining the morphology and size of the organoids. However, segmenting individual tumor organoids is challenging due to their inhomogeneous internal intensity and overlapping structures. This paper proposes a convexity-preserving level-set segmentation 4 model based on the characteristics of tumor organoid images to segment individual tumor organoids precisely. Considering the predominant spherical shape exhibited by organoid growth, we propose a level-set model that includes a data-driven term, a curvature term, and a regularization term. The data-driven term pulls the contour to the vicinity of the boundary; the curvature term ensures the maintenance of convexity in the targeted segmentation, and the regularization term controls the smoothness and propagation of the contour. The proposed model aids in overcoming interference from factors such as overlap and noise, enabling the evolving curve to converge to the actual boundary of the target accurately. Furthermore, we propose a selectable and targeted initialization method that guarantees precise segmentation of specific regions of interest. Experiments on 51 pancreatic ductal adenocarcinoma organoid images show that our model achieved excellent segmentation results. The average Dice value and computation time are 98.81±0.48% and 20.67 s. Compared with the C-V and CPLSE models, it is more accurate and takes less time.

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Section: Introductionmentioning

confidence: 99%

A Convexity-Preserving Level-Set Method for the Segmentation of Tumor Organoids

Lei,

Gui,

Liu

2024

Bioengineering

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“…Instance segmentation is designed to identify and segment pixels that belong to each object instance (Gu et al 2022). Going further, panoptic segmentation includes semantic segmentation and instance segmentation (Chuang et al 2023). Recently, with the rapid development of modern agriculture based on information technology, image segmentation is widely used to monitor crop and soil health, predict the best time to sow, fertilize and harvest, estimate crop yields, and detect plant diseases (Elbasi et al 2023;Wang et al 2022a, b).…”

Section: Introductionmentioning

confidence: 99%

Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

Lei,

Yang,

Yang

et al. 2024

Artif Intell Rev

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Image segmentation is a crucial task in computer vision, which divides a digital image into multiple segments and objects. In agriculture, image segmentation is extensively used for crop and soil monitoring, predicting the best times to sow, fertilize, and harvest, estimating crop yield, and detecting plant diseases. However, image segmentation faces difficulties in agriculture, such as the challenges of disease staging recognition, labeling inconsistency, and changes in plant morphology with the environment. Consequently, we have conducted a comprehensive review of image segmentation techniques based on deep learning, exploring the development and prospects of image segmentation in agriculture. Deep learning-based image segmentation solutions widely used in agriculture are categorized into eight main groups: encoder-decoder structures, multi-scale and pyramid-based methods, dilated convolutional networks, visual attention models, generative adversarial networks, graph neural networks, instance segmentation networks, and transformer-based models. In addition, the applications of image segmentation methods in agriculture are presented, such as plant disease detection, weed identification, crop growth monitoring, crop yield estimation, and counting. Furthermore, a collection of publicly available plant image segmentation datasets has been reviewed, and the evaluation and comparison of performance for image segmentation algorithms have been conducted on benchmark datasets. Finally, there is a discussion of the challenges and future prospects of image segmentation in agriculture.

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“…It offers support for three distinct types of segmentation: semantic segmentation, instance segmentation, and panoptic segmentation [8]. Panoptic segmentation aims to establish a comprehensive framework that bridges the gap between instance and semantic segmentation [9,10]. Its advanced algorithms and pre-trained models make it a powerful tool for medical image analysis.…”

Section: Introductionmentioning

confidence: 99%

Leveraging AI in Postgraduate Medical Education for Rapid Skill Acquisition in Ultrasound-Guided Procedural Techniques

Xu,

Choo,

Ting

et al. 2023

J. Imaging

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Ultrasound-guided techniques are increasingly prevalent and represent a gold standard of care. Skills such as needle visualisation, optimising the target image and directing the needle require deliberate practice. However, training opportunities remain limited by patient case load and safety considerations. Hence, there is a genuine and urgent need for trainees to attain accelerated skill acquisition in a time- and cost-efficient manner that minimises risk to patients. We propose a two-step solution: First, we have created an agar phantom model that simulates human tissue and structures like vessels and nerve bundles. Moreover, we have adopted deep learning techniques to provide trainees with live visualisation of target structures and automate assessment of their user speed and accuracy. Key structures like the needle tip, needle body, target blood vessels, and nerve bundles, are delineated in colour on the processed image, providing an opportunity for real-time guidance of needle positioning and target structure penetration. Quantitative feedback on user speed (time taken for target penetration), accuracy (penetration of correct target), and efficacy in needle positioning (percentage of frames where the full needle is visualised in a longitudinal plane) are also assessable using our model. Our program was able to demonstrate a sensitivity of 99.31%, specificity of 69.23%, accuracy of 91.33%, precision of 89.94%, recall of 99.31%, and F1 score of 0.94 in automated image labelling.

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Deep learning‐based panoptic segmentation: Recent advances and perspectives

Cited by 7 publications

References 137 publications

A Convexity-Preserving Level-Set Method for the Segmentation of Tumor Organoids

A Convexity-Preserving Level-Set Method for the Segmentation of Tumor Organoids

Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

Leveraging AI in Postgraduate Medical Education for Rapid Skill Acquisition in Ultrasound-Guided Procedural Techniques

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