Automatic Segmentation of Color Tongue Image Using Deep Asymmetric Convolution Skip Net

2021 ◽  
Vol 11 (8) ◽  
pp. 2100-2108
Author(s):  
He Huang ◽  
Xi Guan ◽  
Wenbo Zhang ◽  
Juhua Zhou ◽  
Bofeng Wu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Color Images ◽  
End To End ◽  
Collection Methods

Segmentation of the tongue body from color images is vital for tongue diagnoses in traditional Chinese medicine. In tongue images, the tongue body is easily confused with the skin and lips, and the shadow also causes incorrect segmentation. To address these issues, we proposed a novel ACS-Net for tongue image segmentation and implemented the end-to-end form. In our ACS-Net architecture, the following innovations proposed: (1) ordinary convolution was replaced with ACB Module, (2) decoder block restores the features extracted by the encoder block, (3) skip connections are implemented between and within blocks. We use our own datasets named S1 and S2 that collected from the partner hospital. The collection methods of these two datasets are different: S1 was collected by professionals while S2 was taken by nurses. The method achieved state-of-the-art results on both two datasets, we use two metrics to reflect the segmentation performance, which are accuracy (acc) and mean intersection over Union (mIoU), in which the acc reaches 0.984 on S1 and 0.981 on S2; the mIoU reaches 0.925 on S1 and 0.958 on S2.

Automatic Segmentation of Tongue Image Based on Res-Unet Approach

2021 ◽  
Vol 11 (8) ◽  
pp. 2167-2176
Author(s):  
Xi Guan ◽  
Wenbo Zhang ◽  
Juhua Zhou ◽  
Bofeng Wu ◽  
He Huang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Automatic Segmentation ◽  
Data Sets ◽  
Challenging Problem ◽  
Residual Network ◽  
Tongue Diagnosis ◽  
Important Position ◽  
Collection Methods

Tongue diagnosis occupies an important position in the field of traditional Chinese medicine and has been developed for thousands of years. Doctors diagnose disease based on tongue images of patients stored in hospital databases. Hence, segmenting the tongue area of the tongue image facilitates the diagnosis and saves space for storing the tongue image. In order to solve such a challenging problem, we put forward a method combing Unet and Res-net for tongue image segmentation and implements the end-to-end form. In our Res-Unet architecture, including four encoder blocks and four decoder blocks, and the residual network (Res-net) block used as the backbone for each block. The upsampling layer restores the features extracted by the sampling layer. We use our own datasets named TongueSet1 (TS1) and Tongueset2 (TS2) that collected from the hospital. The collection methods of these two datasets are different; TS1 is collected by professionals while TS2 is taken by nurses. This method obtained the latest results on both data sets. We used accuracy (acc) and mean intersection (mIoU) as the evaluation indicators of our model. Among them, the acc and mIoU of the model on TongueSet1 reached 0.984 and 0.925, on TongueSet2 reached 0.985 and 0.925.

Knowledge discovery in traditional Chinese medicine: State of the art and perspectives

2006 ◽  
Vol 38 (3) ◽  
pp. 219-236 ◽  
Author(s):  
Yi Feng ◽  
Zhaohui Wu ◽  
Xuezhong Zhou ◽  
Zhongmei Zhou ◽  
Weiyu Fan
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Knowledge Discovery ◽  
State Of The Art

scholarly journals Mo.Se.: Mosaic image segmentation based on deep cascading learning

2021 ◽  
Vol 12 (24) ◽  
pp. 25
Author(s):  
Andrea Felicetti ◽  
Marina Paolanti ◽  
Primo Zingaretti ◽  
Roberto Pierdicca ◽  
Eva Savina Malinverni
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Archaeological Site ◽  
Visual Observation ◽  
Digital Version ◽  
Generalized Framework ◽  
Close Range ◽  
Processing Techniques

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p class="VARAbstract">Mosaic is an ancient type of art used to create decorative images or patterns combining small components. A digital version of a mosaic can be useful for archaeologists, scholars and restorers who are interested in studying, comparing and preserving mosaics. Nowadays, archaeologists base their studies mainly on manual operation and visual observation that, although still fundamental, should be supported by an automatized procedure of information extraction. In this context, this research explains improvements which can change the manual and time-consuming procedure of mosaic tesserae drawing. More specifically, this paper analyses the advantages of using Mo.Se. (Mosaic Segmentation), an algorithm that exploits deep learning and image segmentation techniques; the methodology combines U-Net 3 Network with the Watershed algorithm. The final purpose is to define a workflow which establishes the steps to perform a robust segmentation and obtain a digital (vector) representation of a mosaic. The detailed approach is presented, and theoretical justifications are provided, building various connections with other models, thus making the workflow both theoretically valuable and practically scalable for medium or large datasets. The automatic segmentation process was tested with the high-resolution orthoimage of an ancient mosaic by following a close-range photogrammetry procedure. Our approach has been tested in the pavement of St. Stephen's Church in Umm ar-Rasas, a Jordan archaeological site, located 30 km southeast of the city of Madaba (Jordan). Experimental results show that this generalized framework yields good performances, obtaining higher accuracy compared with other state-of-the-art approaches. Mo.Se. has been validated using publicly available datasets as a benchmark, demonstrating that the combination of learning-based methods with procedural ones enhances segmentation performance in terms of overall accuracy, which is almost 10% higher. This study’s ambitious aim is to provide archaeologists with a tool which accelerates their work of automatically extracting ancient geometric mosaics.</p><p><strong>Highlights:</strong></p><ul><li><p>A Mo.Se. (Mosaic Segmentation) algorithm is described with the purpose to perform robust image segmentation to automatically detect tesserae in ancient mosaics.</p></li><li><p>This research aims to overcome manual and time-consuming procedure of tesserae segmentation by proposing an approach that uses deep learning and image processing techniques, obtaining a digital replica of a mosaic.</p></li><li><p>Extensive experiments show that the proposed framework outperforms state-of-the-art methods with higher accuracy, even compared with publicly available datasets.</p></li></ul></div></div></div>

scholarly journals Traditional Chinese medicine clinical records classification with BERT and domain specific corpora

2019 ◽  
Vol 26 (12) ◽  
pp. 1632-1636 ◽  
Author(s):  
Liang Yao ◽  
Zhe Jin ◽  
Chengsheng Mao ◽  
Yin Zhang ◽  
Yuan Luo
Keyword(s):  
Deep Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
State Of The Art ◽  
Language Model ◽  
Learning Models ◽  
Data Set ◽  
Clinical Text ◽  
Text Classifiers ◽  
Clinical Records

Abstract Traditional Chinese Medicine (TCM) has been developed for several thousand years and plays a significant role in health care for Chinese people. This paper studies the problem of classifying TCM clinical records into 5 main disease categories in TCM. We explored a number of state-of-the-art deep learning models and found that the recent Bidirectional Encoder Representations from Transformers can achieve better results than other deep learning models and other state-of-the-art methods. We further utilized an unlabeled clinical corpus to fine-tune the BERT language model before training the text classifier. The method only uses Chinese characters in clinical text as input without preprocessing or feature engineering. We evaluated deep learning models and traditional text classifiers on a benchmark data set. Our method achieves a state-of-the-art accuracy 89.39% ± 0.35%, Macro F1 score 88.64% ± 0.40% and Micro F1 score 89.39% ± 0.35%. We also visualized attention weights in our method, which can reveal indicative characters in clinical text.

scholarly journals Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks

2020 ◽  
Vol 6 (7) ◽  
pp. 65 ◽  
Author(s):  
Sulaiman Vesal ◽  
Andreas Maier ◽  
Nishant Ravikumar
Keyword(s):  
Cardiac Mri ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Learning Approaches ◽  
Target Structure ◽  
Low Contrast ◽  
Fully Convolutional Networks ◽  
Multi Stage ◽  
Density Map ◽  
End To End

Cardiac magnetic resonance (CMR) imaging is used widely for morphological assessment and diagnosis of various cardiovascular diseases. Deep learning approaches based on 3D fully convolutional networks (FCNs), have improved state-of-the-art segmentation performance in CMR images. However, previous methods have employed several pre-processing steps and have focused primarily on segmenting low-resolutions images. A crucial step in any automatic segmentation approach is to first localize the cardiac structure of interest within the MRI volume, to reduce false positives and computational complexity. In this paper, we propose two strategies for localizing and segmenting the heart ventricles and myocardium, termed multi-stage and end-to-end, using a 3D convolutional neural network. Our method consists of an encoder–decoder network that is first trained to predict a coarse localized density map of the target structure at a low resolution. Subsequently, a second similar network employs this coarse density map to crop the image at a higher resolution, and consequently, segment the target structure. For the latter, the same two-stage architecture is trained end-to-end. The 3D U-Net with some architectural changes (referred to as 3D DR-UNet) was used as the base architecture in this framework for both the multi-stage and end-to-end strategies. Moreover, we investigate whether the incorporation of coarse features improves the segmentation. We evaluate the two proposed segmentation strategies on two cardiac MRI datasets, namely, the Automatic Cardiac Segmentation Challenge (ACDC) STACOM 2017, and Left Atrium Segmentation Challenge (LASC) STACOM 2018. Extensive experiments and comparisons with other state-of-the-art methods indicate that the proposed multi-stage framework consistently outperforms the rest in terms of several segmentation metrics. The experimental results highlight the robustness of the proposed approach, and its ability to generate accurate high-resolution segmentations, despite the presence of varying degrees of pathology-induced changes to cardiac morphology and image appearance, low contrast, and noise in the CMR volumes.

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