Multimodal medical image segmentation using multi-scale context-aware network

Wang, Xue; Li, Zhanshan; Huang, Yongping; Jiao, Yingying

doi:10.1016/j.neucom.2021.11.017

Cited by 40 publications

(14 citation statements)

References 48 publications

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“…Deep learning also enables robots to sense and process various information in surgical scenes so that they can better execute the operation; this comes with improvements in surgical precision and efficiency. [15][16][17]…”

Section: 1mentioning

confidence: 99%

“…Moreover, they can also help surgeons accurately recognize and localize the organs and structures they need to operate on, thus preventing mistakes or damaging surrounding tissues. Deep learning also enables robots to sense and process various information in surgical scenes so that they can better execute the operation; this comes with improvements in surgical precision and efficiency 15‐17 Increasing surgery automation …”

Section: Introductionmentioning

confidence: 99%

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Deep learning‐based intraoperative video analysis for supporting surgery

Bai

2023

Concurrency and Computation

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SummaryThe application of surgical tool detection and tracking and surgical phase recognition is research hotspots at present. Surgical tool detection and tracking provides accurate real‐time information to surgeons, helping them plan surgeries and get advance warnings so that procedures can be safer. Surgical phase recognition can help improve the surgery efficiency, facilitate surgeons to make correct decisions, and help evaluate surgeon skill. In this article, we described the advantages and disadvantages of applying deep learning to surgical procedures. Then, we analyzed the advantages of deep learning in visual analysis, summarized the algorithms typically used in surgical tool detection and tracking and surgical phase recognition, and reviewed the results of these algorithms. The main problems are disclosed and the development trend of deep learning‐based intraoperative video analysis for supporting surgery is briefly discussed.

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Section: 1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning‐based intraoperative video analysis for supporting surgery

Bai

2023

Concurrency and Computation

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“…Wang et al. ( 2022 ) proposed dense skip connections in multimodal medical image segmentation, thus retaining more contextual information and using multilevel features to help recover images. To facilitate the classification of breast cancer in histopathological images, Chattopadhyay et al.…”

Section: Related Workmentioning

confidence: 99%

PDRF-Net: a progressive dense residual fusion network for COVID-19 lung CT image segmentation

Zhang

Yuan

2023

Evolving Systems

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The lungs of patients with COVID-19 exhibit distinctive lesion features in chest CT images. Fast and accurate segmentation of lesion sites from CT images of patients’ lungs is significant for the diagnosis and monitoring of COVID-19 patients. To this end, we propose a progressive dense residual fusion network named PDRF-Net for COVID-19 lung CT segmentation. Dense skip connections are introduced to capture multi-level contextual information and compensate for the feature loss problem in network delivery. The efficient aggregated residual module is designed for the encoding-decoding structure, which combines a visual transformer and the residual block to enable the network to extract richer and minute-detail features from CT images. Furthermore, we introduce a bilateral channel pixel weighted module to progressively fuse the feature maps obtained from multiple branches. The proposed PDRF-Net obtains good segmentation results on two COVID-19 datasets. Its segmentation performance is superior to baseline by 11.6% and 11.1%, and outperforming other comparative mainstream methods. Thus, PDRF-Net serves as an easy-to-train, high-performance deep learning model that can realize effective segmentation of the COVID-19 lung CT images.

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“…For multimodal medical picture segmentation, a multi-scale context-aware network (CA-Net) is suggested that collects rich contextual information with dense skip connections and gives varying weights to various channels. Four essential parts make up CA-Net: an encoder module, a decoder module, a multi-scale context fusion (MCF) module, and a dense skip connection module [78]. For the goal of segmenting medical images, a scalable functional variational Bayesian neural network (BNN) containing Gaussian processes (GPs) is presented.…”

Section: Neurosciencementioning

confidence: 99%

Image Segmentation Using Hybrid Optimization Algorithms: Review

Hemeida¹,

Yahya

Al-Sanary

2022

Aswan University Journal of Sciences and Technology

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Image segmentation, often based on the properties of image pixels, is a widely used method in digital image processing and analysis to divide an image into multiple parts or areas. The main goal of image segmentation is to simplify the image for easier analysis. It would be very difficult to implement computer vision without performing image segmentation. An important component of computer vision is image segmentation, which has numerous commercial applications. Google and others Image-based search engines use image segmentation algorithms to identify the objects in your image and match their results to the relevant images they find to present you with search results.

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Multimodal medical image segmentation using multi-scale context-aware network

Cited by 40 publications

References 48 publications

Deep learning‐based intraoperative video analysis for supporting surgery

Deep learning‐based intraoperative video analysis for supporting surgery

PDRF-Net: a progressive dense residual fusion network for COVID-19 lung CT image segmentation

Image Segmentation Using Hybrid Optimization Algorithms: Review

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