Multi‐Scale 3D U‐Nets: An approach to automatic segmentation of brain tumor

Peng, Suting; Chen, Wei; Sun, Jiawei; Liu, Boqiang

doi:10.1002/ima.22368

Cited by 41 publications

(24 citation statements)

References 24 publications

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“…The strategy encodes progressively abstract interpretations of the input as we move deeper and adds a localization pathway that recombines these interpretations with features for lower layers. By hypothesizing that semantic features are easy to learn and process, Peng et al [ 60 ] presented a multi-scale 3D U-Net for brain tumor segmentation. Their model consists of several 3D U-Net blocks for capturing long-distance spatial resolutions.…”

Section: Applications In 3d Medical Imagingmentioning

confidence: 99%

3D Deep Learning on Medical Images: A Review

Singh

Wang

Gupta

et al. 2020

Sensors

388

150

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The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.

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Section: Applications In 3d Medical Imagingmentioning

confidence: 99%

3D Deep Learning on Medical Images: A Review

Singh

Wang

Gupta

et al. 2020

Sensors

388

150

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show abstract

“…The U-Net architecture was adapted to the three-dimensional medical images as a 3D U-Net [28][29][30] or V-Net [31] network. To adapt U-Net to the three-dimensional data, all convolutional operations were replaced by three-dimensional convolutions.…”

Section: Segmentation Taskmentioning

confidence: 99%

Overview of the Three-dimensional Convolutional Neural Networks Usage in Medical Computer-aided Diagnosis Systems

Chapaliuk¹

2020

AJNNA

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Medical computer-aided diagnosis systems are essential applications that help doctors speed up, standardize, and improve disease prediction quality. Nevertheless, it is hard to implement a high-accuracy diagnosis system due to complex medical data structures that are hard to interpret even by an experienced radiologist, lack of the labeled data, and the high-resolution three-dimensional nature of the data. Meanwhile, modern deep learning methods achieved a significant breakthrough in various computer vision tasks. Thus, the same methods began to gain popularity in the community that works on the computer-aided systems implementation. Most modern diagnosis systems work with three-dimensional medical images that cannot be processed by traditional two-dimensional convolutional neural networks to get high enough prediction results. Hence, medical research introduced new methods that use three-dimensional neural networks to work with medical images. Even though these networks are usually an adapted version of state-of-the-art two-dimensional networks, they still have their specifics and modifications that help achieve human-level accuracy and should be considered separately. This article overviews the three-dimensional convolutional neural networks and how they are different from their two-dimensional versions. Moreover, the article examines the most influenced systems that achieve human-level accuracy in predicting the specific disease. The networks discussed in the perspective of two basic tasks: segmentation and classification. That is because the simple end-to-end classification neural networks usually do not work well on the available amount of data in the medical domain.

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“…A number of artificial intelligence (AI) systems based on deep learning have been proposed and results have been shown to be quite promising in medical image analysis 13‐16 . Compared to the traditional imaging workflow heavily relies on the human labors, AI enables more safe, accurate, and efficient imaging solutions.…”

Section: Introductionmentioning

confidence: 99%

Automatic COVID‐19 CT segmentation using U‐Net integrated spatial and channel attention mechanism

Zhou

Canu

Ruan

2020

Int J Imaging Syst Tech

153

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The coronavirus disease (COVID‐19) pandemic has led to a devastating effect on the global public health. Computed Tomography (CT) is an effective tool in the screening of COVID‐19. It is of great importance to rapidly and accurately segment COVID‐19 from CT to help diagnostic and patient monitoring. In this paper, we propose a U‐Net based segmentation network using attention mechanism. As not all the features extracted from the encoders are useful for segmentation, we propose to incorporate an attention mechanism including a spatial attention module and a channel attention module, to a U‐Net architecture to re‐weight the feature representation spatially and channel‐wise to capture rich contextual relationships for better feature representation. In addition, the focal Tversky loss is introduced to deal with small lesion segmentation. The experiment results, evaluated on a COVID‐19 CT segmentation dataset where 473 CT slices are available, demonstrate the proposed method can achieve an accurate and rapid segmentation result on COVID‐19. The method takes only 0.29 second to segment a single CT slice. The obtained Dice Score and Hausdorff Distance are 83.1% and 18.8, respectively.

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Multi‐Scale 3D U‐Nets: An approach to automatic segmentation of brain tumor

Cited by 41 publications

References 24 publications

3D Deep Learning on Medical Images: A Review

3D Deep Learning on Medical Images: A Review

Overview of the Three-dimensional Convolutional Neural Networks Usage in Medical Computer-aided Diagnosis Systems

Automatic COVID‐19 CT segmentation using U‐Net integrated spatial and channel attention mechanism

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