Multi-Scale Context-Guided Deep Network for Automated Lesion Segmentation With Endoscopy Images of Gastrointestinal Tract

Wang, Shuai; Cong, Yang; Zhu, Hancan; Chen, Xianyi; Qu, Liangqiong; Fan, Huijie; Zhang, Qiang; Liu, Mingxia

doi:10.1109/jbhi.2020.2997760

Cited by 81 publications

(25 citation statements)

References 63 publications

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“…To compare the segmented image structures, the structural similitude index (SSIM) is used. The higher SSIM number, the better the original image segmentation [ 43 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

An Efficient Adaptive Salp Swarm Algorithm Using Type II Fuzzy Entropy for Multilevel Thresholding Image Segmentation

Mahajan

Mittal

Salgotra

et al. 2022

Computational and Mathematical Methods in Medicine

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Salp swarm algorithm (SSA) is an innovative contribution to smart swarm algorithms and has shown its utility in a wide range of research domains. While it is an efficient algorithm, it is noted that SSA suffers from several issues, including weak exploitation, convergence, and unstable exploitation and exploration. To overcome these, an improved SSA called as adaptive salp swarm algorithm (ASSA) was proposed. Thresholding is among the most effective image segmentation methods in which the objective function is described in relation of threshold values and their position in the histogram. Only if one threshold is assumed, a segmented image of two groups is obtained. But on other side, several groups in the output image are generated with multilevel thresholds. The methods proposed by authors previously were traditional measures to identify objective functions. However, the basic challenge with thresholding methods is defining the threshold numbers that the individual must choose. In this paper, ASSA, along with type II fuzzy entropy, is proposed. The technique presented is examined in context with multilevel image thresholding, specifically with ASSA. For this reason, the proposed method is tested using various images simultaneously with histograms. For evaluating the performance efficiency of the proposed method, the results are compared, and robustness is tested with the efficiency of the proposed method to multilevel segmentation of image; numerous images are utilized arbitrarily from datasets.

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“…To compare the segmented image structures, the structural similitude index (SSIM) is used. The higher SSIM number, the better the original image segmentation [ 43 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

An Efficient Adaptive Salp Swarm Algorithm Using Type II Fuzzy Entropy for Multilevel Thresholding Image Segmentation

Mahajan

Mittal

Salgotra

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Pre-trained models such as Google net and Alex net are trained on the stomach ulcer datasets on 0.01 learning rate that does not provides satisfactory classification outcomes [35]. The MCNet does not provide accurate lesions segmentation due to unclear boundaries among the infected and the healthy regions [49].…”

Section: Related Workmentioning

confidence: 99%

3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks

Amin

Sharif

Gul

et al. 2021

Complex Intell. Syst.

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Wireless capsule endoscopy (WCE) might move through human body and captures the small bowel and captures the video and require the analysis of all frames of video due to which the diagnosis of gastrointestinal infections by the physician is a tedious task. This tiresome assignment has fuelled the researcher’s efforts to present an automated technique for gastrointestinal infections detection. The segmentation of stomach infections is a challenging task because the lesion region having low contrast and irregular shape and size. To handle this challenging task, in this research work a new deep semantic segmentation model is suggested for 3D-segmentation of the different types of stomach infections. In the segmentation model, deep labv3 is employed as a backbone of the ResNet-50 model. The model is trained with ground-masks and accurately performs pixel-wise classification in the testing phase. Similarity among the different types of stomach lesions accurate classification is a difficult task, which is addressed in this reported research by extracting deep features from global input images using a pre-trained ResNet-50 model. Furthermore, the latest advances in the estimation of uncertainty and model interpretability in the classification of different types of stomach infections is presented. The classification results estimate uncertainty related to the vital features in input and show how uncertainty and interpretability might be modeled in ResNet-50 for the classification of the different types of stomach infections. The proposed model achieved up to 90% prediction scores to authenticate the method performance.

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“…Ali et.al 21 presented a comprehensive analysis of various approaches that were submitted to EAD2020 challenge for artefact detection and segmentation and EDD2020 challenge for disease detection and segmentation. A multi-scale context guided deep network based on FCN was proposed by Wang et.al 22 for lesion segmentation in endoscopy images of Gastrointestinal (GI) tract. Jha et.…”

Section: Semantic Segmentationmentioning

confidence: 99%

Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy

Gupta¹,

Ali²,

Goldsmith³

et al. 2021

Preprint

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Kidney stones represent a considerable burden for public health-care systems, with the total health-care expenditure for kidney stones exceeding US $ 2 billion annually in the USA alone. Ureteroscopy with laser lithotripsy has evolved as the most commonly used technique for the treatment of kidney stones. Automated segmentation of kidney stones and laser fiber is an important initial step to performing any automated quantitative analysis of the stones, particularly stone-size estimation, that can be used by the surgeon to decide if the stone requires further fragmentation. Factors such as turbid fluid inside the cavity, specularities, motion blur due to kidney movements and camera motion, bleeding, and stone debris impact the quality of vision within the kidney and lead to extended operative times. To the best of our knowledge, this is the first attempt made towards multi-class segmentation in ureteroscopy and laser lithotripsy data. We propose an end-to-end convolution neural network (CNN) based learning framework for the segmentation of stones and laser fiber. The proposed approach utilizes two sub-networks: I) HybResUNet, a hybrid version of residual U-Net, that uses residual connections in the encoder path of U-Net to improve semantic predictions, and II) a DVFNet that generates deformation vector field (DVF) predictions by leveraging motion differences between the adjacent video frames which is then used to prune the prediction maps. We also present ablation studies that combine different dilated convolutions, recurrent and residual connections, atrous spatial pyramid pooling and attention gate model. Further, we propose a compound loss function that significantly boosts the segmentation performance in our data. We have also provided an ablation study to determine the optimal data augmentation strategy for our dataset. Our qualitative and quantitative results illustrate that our proposed method outperforms state-of-the-art methods such as UNet and DeepLabv3+ showing an improvement of 5.2% and 15.93%, respectively, for the combined mean of DSC and JI in our in vivo test dataset. We also show that our proposed model generalizes better on a new clinical dataset showing a mean improvement of 25.4%, 20%, and 11% over UNet, HybResUNet, and DeepLabv3+, respectively, for the same metric.

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Multi-Scale Context-Guided Deep Network for Automated Lesion Segmentation With Endoscopy Images of Gastrointestinal Tract

Cited by 81 publications

References 63 publications

An Efficient Adaptive Salp Swarm Algorithm Using Type II Fuzzy Entropy for Multilevel Thresholding Image Segmentation

An Efficient Adaptive Salp Swarm Algorithm Using Type II Fuzzy Entropy for Multilevel Thresholding Image Segmentation

3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks

Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy

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