Retinal vessel optical coherence tomography images for anemia screening

Chen, Zailiang; Mo, Yufang; Ouyang, Pingbo; Shen, Hailan; Li, Dabao; Zhao, Rongchang

doi:10.1007/s11517-018-1927-8

Cited by 25 publications

(17 citation statements)

References 35 publications

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“…Spectral domain optical coherence tomography (SD-OCT) is a widely used imaging modality in ophthalmology for observing, assessing, and diagnosing retinal diseases, such as glaucoma, macular degeneration, anemia and diabetic retinopathy (DR) [1], [2]. Unfortunately, almost all eye diseases can occur in people with diabetes, which is a widespread metabolic disease that affects approximately 415 million people (about 8.3% of the global adult population) [3].…”

Section: Introductionmentioning

confidence: 99%

The Segmentation of Retinal Layer and Fluid in SD-OCT Images Using Mutex Dice Loss Based Fully Convolutional Networks

Wei

Peng

2020

IEEE Access

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Spectral domain optical coherence tomography (SD-OCT) is a non-invasive imaging modality for assessing retinal diseases, such as diabetic retinopathy (DR), which is one of the most prevalent diseases responsible for visual impairment and blindness in the world. The main manifestations of DR are retinal deformation and fluid masses, termed diabetic macular edema (DME), which is the primary biomarker for assessing and diagnosing diseases. In the clinic, ophthalmologists can manually segment retinal layers and fluids to get quantitative and diagnostic information, which is the basement of the final diagnosis. However, this manual segmentation is time-consuming and labor-intensive. To facilitate and promote it, researchers have proposed many automated methods, where most of them usually ignore the priorities in ophthalmology and just regard this task as a standard semantic segmentation task. In this study, we consider the priority of the mutex relationship among different layers and introduce it into the dice loss function to build a novel one, named mutex dice loss (MDL). Besides, we propose a novel fully convolutional network based on our proposed depth max pooling (DMP) to segment retinal layers and fluids in SD-OCT images. Experimental results of the proposed method on two public datasets demonstrate promising performance, which also shows the potential to help ophthalmologists in the diagnostic process of DR or other related diseases. INDEX TERMS Diabetic macular edema (DME), retinal layer segmentation, fully convolutional network, mutex dice loss.

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Section: Introductionmentioning

confidence: 99%

The Segmentation of Retinal Layer and Fluid in SD-OCT Images Using Mutex Dice Loss Based Fully Convolutional Networks

Wei

Peng

2020

IEEE Access

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“…Experimental results demonstrated an 83.6% accuracy, suggesting potential for future clinical utilization. [ 34 ] Ashok et al combined Raman spectroscopy and OCT for colorectal cancer diagnosis. [ 35 ] With the help of texture features, a sensitivity and specificity of ~94% was achieved for cancer vs normal snap‐frozen tissue samples.…”

Section: Introductionmentioning

confidence: 99%

Diagnosing colorectal abnormalities using scattering coefficient maps acquired from optical coherence tomography

Zeng

Chapman

Lin

et al. 2020

Journal of Biophotonics

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Optical coherence tomography (OCT) has shown potential in differentiating normal colonic mucosa from neoplasia. In this study of 33 fresh human colon specimens, we report the first use of texture features and computer vision-based imaging features acquired from en face scattering coefficient maps to characterize colorectal tissue. En face scattering coefficient maps were generated automatically using a new fast integral imaging algorithm. From these maps, a gray-level cooccurrence matrix algorithm was used to extract texture features, and a scale-invariant feature transform algorithm was used to derive novel computer vision-based features. In total, 25 features were obtained, and the importance of each feature in diagnosis was evaluated using a random forest model. Two classifiers were assessed on two different classification tasks. A support vector machine model was found to be optimal for distinguishing normal from abnormal tissue, with 94.7% sensitivity and 94.0% specificity, while a random forest model performed optimally in further differentiating abnormal tissues (i.e., cancerous tissue and adenomatous polyp) with 86.9% sensitivity and 85.0% specificity. These results demonstrated the potential of using OCT to aid the diagnosis of human colorectal disease.

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“…Therefore, WSNs are widely used in many fields, such as medical, military, environmental monitoring. [21][22][23][24][25][26][27][28][29] According to research, 30 the loss ratio of wireless communication is several orders of magnitude different from that of wired communication. The loss ratio of wireless transmission is even as high as 20% or more.…”

Section: Introductionmentioning

confidence: 99%

Adaptive duty cycle control–based opportunistic routing scheme to reduce delay in cyber physical systems

Xiang

Liu

et al. 2019

International Journal of Distributed Sensor Networks

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Wireless sensor networks are the foundation of cyber physical systems. In loss and delay sensitive wireless sensor networks, there is much room for improving the performance of opportunistic routing scheme. In this article, an adaptive duty cycle control-based opportunistic routing scheme was put forward to improve the performance of the opportunistic routing scheme. Our main work is as follows: (a) the proposed duty cycle control-based opportunistic routing scheme implements the opportunistic routing in the duty cycle-based wireless sensor networks. Compared with the opportunistic routing scheme in the non-duty cycle mode, the duty cycle control-based opportunistic routing scheme can effectively reduce the energy consumption by 55.64%, although the performance in the delay is degraded. (b) Next, adaptive duty cycle control-based opportunistic routing scheme is proposed to reduce the delay of nodes in duty cycle control-based opportunistic routing. In adaptive duty cycle control-based opportunistic routing scheme, the node's duty cycle is increased in the area where the energy has remaining, so that the network delay can be reduced without reducing the network lifetime. Compared with duty cycle control-based opportunistic routing scheme, the maximum end-toend delay of the node in the network can be reduced by 69.83%.

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Retinal vessel optical coherence tomography images for anemia screening

Cited by 25 publications

References 35 publications

The Segmentation of Retinal Layer and Fluid in SD-OCT Images Using Mutex Dice Loss Based Fully Convolutional Networks

The Segmentation of Retinal Layer and Fluid in SD-OCT Images Using Mutex Dice Loss Based Fully Convolutional Networks

Diagnosing colorectal abnormalities using scattering coefficient maps acquired from optical coherence tomography

Adaptive duty cycle control–based opportunistic routing scheme to reduce delay in cyber physical systems

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