A multi-scale recurrent fully convolution neural network for laryngeal leukoplakia segmentation

Ji, Bin; Ren, Jianjun; Zheng, Xiujuan; Tan, Cong; Ji, Rong; Zhao, Yu; Liu, Kai

doi:10.1016/j.bspc.2020.101913

Cited by 21 publications

(12 citation statements)

References 34 publications

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“…Endoscopy is an invasive imaging procedure in which the imaging device is inserted into an organ or cavity to take pictures. U-net has been applied to endoscopy images for segmentation of polyps in the gastrointestinal tract [97], [274], [301], [334], colon objects [59], detection of laryngeal leukoplakia [65], and detection of surgical instruments [335]. On electron microscopy images, applications include the detection of neuronal structures [161], [336], cell contour [161], [201], [232], and viruses [337].…”

Section: H Other Modalitiesmentioning

confidence: 99%

“…In this expression x f (t) is the feedforward input and x r (t-1) is the recurrent input for the l th layer, w f is the feedforward weight, w r is the recurrent weight, and b is the bias of the k th feature map. Recurrent convolutional layers have been used in [64], [65]. Alom et al [52], [66] devised a U-net model containing both recurrent convolution layers and residual connections.…”

Section: Recurrent Convolutional Networkmentioning

confidence: 99%

“…Model/Methods used Image Modality [103], [237] Base U-net Endoscopy [59] Residual block Endoscopy [65] Cascaded U-net; Recurrent residual net Endoscopy [97] Modified U-net with parallel decoders Endoscopy [156], [238], [239] Base U-net Electron microscopy [169] Residual block Electron microscopy [240]- [244] Base U-net OCT [245] Base U-net DSA [68] Dense block DSA [30] 3D U-net DTI [37] Attention gate Iris imaging [56] Residual block Mammogram…”

Section: Referencementioning

confidence: 99%

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U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: H Other Modalitiesmentioning

confidence: 99%

Section: Recurrent Convolutional Networkmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

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U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…The application of UNet for 2D medical image segmentation covers a range of tasks, including skin lesion segmentation [ 33 ], segmentation in microscopy [ 16 , 34 , 35 , 36 ], and retinal imaging [ 37 , 38 , 39 , 40 ] to name a few. Endoscopic imaging is the modality of interest for 2D image segmentation, with several uses of UNet for these types of images [ 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

Local Style Preservation in Improved GAN-Driven Synthetic Image Generation for Endoscopic Tool Segmentation

Jiang

Chitrakar

et al. 2021

Sensors

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Accurate semantic image segmentation from medical imaging can enable intelligent vision-based assistance in robot-assisted minimally invasive surgery. The human body and surgical procedures are highly dynamic. While machine-vision presents a promising approach, sufficiently large training image sets for robust performance are either costly or unavailable. This work examines three novel generative adversarial network (GAN) methods of providing usable synthetic tool images using only surgical background images and a few real tool images. The best of these three novel approaches generates realistic tool textures while preserving local background content by incorporating both a style preservation and a content loss component into the proposed multi-level loss function. The approach is quantitatively evaluated, and results suggest that the synthetically generated training tool images enhance UNet tool segmentation performance. More specifically, with a random set of 100 cadaver and live endoscopic images from the University of Washington Sinus Dataset, the UNet trained with synthetically generated images using the presented method resulted in 35.7% and 30.6% improvement over using purely real images in mean Dice coefficient and Intersection over Union scores, respectively. This study is promising towards the use of more widely available and routine screening endoscopy to preoperatively generate synthetic training tool images for intraoperative UNet tool segmentation.

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“…Bin Ji et al . [24] reported a multi-scale recurrent fully convolution neural network (CNN) for laryngeal leukoplakia segmentation. Despite favorable results, their datasets included only static images taken by WLI under optimal conditions whereas NBI is crucial for the differentiation of benign from malignant lesions.…”

Section: Discussionmentioning

confidence: 99%

Applying Deep Learning with Convolutional Neural Networks to Laryngoscopic Imaging for Real-time Automated Segmentation and Classification of Vocal Cord Leukoplakia

Luo

Ren

et al. 2021

Preprint

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Objectives The study was to apply deep learning (DL) with convolutional neural networks (CNNs) to laryngoscopic imaging for assisting in real-time automated segmentation and classification of vocal cord leukoplakia. Methods This was a single-center retrospective diagnostic study included 216 patients who underwent laryngoscope and pathological examination from October 1, 2018 through October 1, 2019. Lesions were classified as nonsurgical group (NSG) and surgical group (SG) according to pathology. All selected images of vocal cord leukoplakia were annotated independently by 2 expert endoscopists and divided into a training set, a validation set, and a test set in a ratio of 6:2:2 for training the model. Results Among the 260 lesions identified in 216 patients, 2220 images from narrow band imaging (NBI) and 2144 images from white light imaging (WLI) were selected. For segmentation, the average intersection-over-union (IoU) value exceeded 70%. For classification, the model was able to classify the surgical group (SG) by laryngoscope with a sensitivity of 0.93 and specificity of 0.94 in WLI, and a sensitivity of 0.99 and specificity of 0.97 in NBI. Moreover, this model achieved a mean average precision (mAP) of 0.81 in WLI and 0.92 in NBI with an IoU> 0.5. Conclusions The study found that a model developed by applying DL with CNNs to laryngoscopic imaging results in high sensitivity, specificity, and mAP for automated segmentation and classification of vocal cord leukoplakia. This finding shows promise for the application of DL with CNNs in assisting in accurate diagnosis of vocal cord leukoplakia from WLI and NBI.

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A multi-scale recurrent fully convolution neural network for laryngeal leukoplakia segmentation

Cited by 21 publications

References 34 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Local Style Preservation in Improved GAN-Driven Synthetic Image Generation for Endoscopic Tool Segmentation

Applying Deep Learning with Convolutional Neural Networks to Laryngoscopic Imaging for Real-time Automated Segmentation and Classification of Vocal Cord Leukoplakia

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