Automatic Acute Ischemic Stroke Lesion Segmentation Using Semi-supervised Learning

Zhao, Bin; Ding, Shuxue; Wu, Hong; Li, Guohua; Cao, Chen; Song, Junqiang; Liu, Zhiyang

doi:10.2991/ijcis.d.210205.001

Cited by 5 publications

(4 citation statements)

References 42 publications

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Section: Clinical Applicationsmentioning

confidence: 99%

“…Liu et al [ 64 ] used a U-shaped network (Res-CNN) to automatically segment acute ischemic stroke lesions from multimodality MRIs, and the average Dice coefficient was 0.742. Zhao et al [ 65 ] proposed a semisupervised learning method using the weakly labeled subjects to detect the suspicious acute ischemic stroke lesions and achieved a mean Dice coefficient of 0.642. Compared to using MRI, a 2D patch-based deep learning approach was proposed to segment the acute stroke lesion core from CT perfusion images [ 66 ], and the average Dice coefficient was 0.49.…”

Section: Clinical Applicationsmentioning

confidence: 99%

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Advances in Deep Learning-Based Medical Image Analysis

et al. 2021

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Importance. With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia. This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications. It also discussed the existing problems in the field and provided possible solutions and future directions. Highlights. This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications. More specifically, state-of-the-art clinical applications include four major human body systems: the nervous system, the cardiovascular system, the digestive system, and the skeletal system. Overall, according to the best available evidence, deep learning models performed well in medical image analysis, but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability. Future direction could include federated learning, benchmark dataset collection, and utilizing domain subject knowledge as priors. Conclusion. Recent advanced deep learning technologies have achieved great success in medical image analysis with high accuracy, efficiency, stability, and scalability. Technological advancements that can alleviate the high demands on high-quality large-scale datasets could be one of the future developments in this area.

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Section: Clinical Applicationsmentioning

confidence: 99%

Section: Clinical Applicationsmentioning

confidence: 99%

Advances in Deep Learning-Based Medical Image Analysis

et al. 2021

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“…However, the fully labeled annotation of lesions from a large number of images is tremendously labor and time intensive. Recently, some weakly supervised approaches were proposed to leverage the annotation workload (12)(13)(14), such as the wiseDNN (15) and the 3D weakly supervised network (16). The related studies have indicated that weakly supervised approaches can reduce the difficulty of label acquisition while still maintaining high detection efficiency.…”

Section: Introductionmentioning

confidence: 99%

Ability of weakly supervised learning to detect acute ischemic stroke and hemorrhagic infarction lesions with diffusion-weighted imaging

Cao¹,

Liu²,

Li³

et al. 2022

Quant Imaging Med Surg

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Background: Gradient-recalled echo (GRE) sequence is time-consuming and not routinely performed.Herein, we aimed to investigate the ability of weakly supervised learning to identify acute ischemic stroke (AIS) and concurrent hemorrhagic infarction based on diffusion-weighted imaging (DWI).Methods: First, we proposed spatially locating small stroke lesions in different positions and hemorrhagic infarction lesions by residual neural and visual geometry group networks using weakly supervised learning.Next, we compared the sensitivity and specificity for identifying automatically concurrent hemorrhagic infarction in stroke patients with the sensitivity and specificity of human readings of diffusion and b 0 images to evaluate the performance of the weakly supervised methods. Also, the labeling time of the weakly supervised approach was compared with that of the fully supervised approach.Results: Data from a total of 1,027 patients were analyzed. The residual neural network displayed a higher sensitivity than did the visual geometry group network in spatially locating the small stroke and hemorrhagic infarction lesions. The residual neural network had significantly greater patient-level sensitivity than did the human readers (98.4% versus 86.2%, P=0.008) in identifying concurrent hemorrhagic infarction with GRE as the reference standard; however, their specificities were comparable (95.4% versus 96.9%, P>0.99). Weak labeling of lesions required significantly less time than did full labeling of lesions (2.667 versus 10.115 minutes, P<0.001).Conclusions: Weakly supervised learning was able to spatially locate small stroke lesions in different positions and showed more sensitivity than did human reading in identifying concurrent hemorrhagic infarction based on DWI. The proposed approach can reduce the labeling workload.

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“…Multiple studies have investigated medical image segmentation [3]- [5]. Recently, deep learning-based approaches have shown extraordinary potential in medical image segmentation tasks, especially for convolutional neural networks (CNNs) [6]- [11].…”

Section: Introductionmentioning

confidence: 99%

SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization

Yu¹,

Huang²,

Zhang³

et al. 2022

Preprint

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There are considerable interests in automatic stroke lesion segmentation on magnetic resonance (MR) images in the medical imaging field, as strokes are the main cause of various cerebrovascular diseases. Although deep learning-based models have been proposed for this task, generalizing these models to unseen sites is difficult due to not only the large intersite discrepancy among different scanners, imaging protocols, and populations but also the variations in stroke lesion shape, size, and location. Thus, we propose a U-net-based segmentation network termed SG-Net to improve unseen site generalization for stroke lesion segmentation on MR images. Specifically, we first propose masked adaptive instance normalization (MAIN) to minimize intersite discrepancies, standardizing input MR images from different sites into a site-unrelated style by dynamically learning affine parameters from the input. Then, we leverage a gradient reversal layer to force the U-net encoder to learn site-invariant representation, which further improves the model generalization in conjunction with MAIN. Finally, inspired by the "pseudosymmetry" of the human brain, we introduce a simple, yet effective data augmentation technique that can be embedded within SG-Net to double the sample size while halving memory consumption. As a result, stroke lesions from the whole brain can be easily identified within a hemisphere, improving the simplicity of training. Experimental results on the benchmark Anatomical Tracings of Lesions After Stroke (ATLAS) dataset, which includes MR images from 9 different sites, demonstrate that under the "leave-one-site-out" setting, the proposed SG-Net substantially outperforms recently published methods in terms of quantitative metrics and qualitative comparisons.

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Automatic Acute Ischemic Stroke Lesion Segmentation Using Semi-supervised Learning

Cited by 5 publications

References 42 publications

Advances in Deep Learning-Based Medical Image Analysis

Advances in Deep Learning-Based Medical Image Analysis

Ability of weakly supervised learning to detect acute ischemic stroke and hemorrhagic infarction lesions with diffusion-weighted imaging

SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization

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