Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology最新文献_第3页

The Role of Color in Palliative Care for Children 颜色在儿童姑息治疗中的作用

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340098

Andrew C. Yang

引用次数: 0

Selective Detection and Segmentation of Cervical Cells 宫颈细胞的选择性检测与分割

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340081

Jing Ke, Zhaoming Jiang, Changchang Liu, T. Bednarz, A. Sowmya, Xiaoyao Liang

{"title":"Selective Detection and Segmentation of Cervical Cells","authors":"Jing Ke, Zhaoming Jiang, Changchang Liu, T. Bednarz, A. Sowmya, Xiaoyao Liang","doi":"10.1145/3340074.3340081","DOIUrl":"https://doi.org/10.1145/3340074.3340081","url":null,"abstract":"Accurate detection and segmentation of cervical cells is often considered as a critical prerequisite of the prediction of dysplasia or cancer either by a pap smear or the lately developed liquid-based cytology (LBC). The computer-aided detection in microscope images can relieve the pathologists from strenuous manual labors with higher accuracy and efficiency. In the segmentation tasks of real-life clinical data, one challenging issue is the mis-identification of other cells, such as inflammatory cells, with similar appearance of nuclei in shape, size and texture. With a large distribution in the whole slide, even overlap up to 50% to 75% percentage of normal or abnormal cells, these cells are usually detected and segmented as nuclei. In this paper, compared with the typical three-catalogue segmentation methods of nuclei, cytoplasm and background proposed in the literature, we provide a discrimination between inflammatory cells and nuclei by adding a new catalogue. We present two novel convolutional neural networks (CNN), a deeply fine-tuned model and a trained from scratch model. The models enable us to sensitively detect and remove background noises such as mucus or red blood cells. We also profile a detailed performance comparison between these two methods, with the advantages of either network presented. The experiments are based on the sufficient clinical dataset we collected, and the results show the effectiveness of proposed approaches in selective cell detection and segmentation.","PeriodicalId":196396,"journal":{"name":"Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122405737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Visualizing the Correspondence of Feature Point Mapping between DICOM Images before and after Surgery 手术前后DICOM图像特征点映射的对应可视化

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340075

H. Noborio, Shota Uchibori, M. Koeda, Kaoru Watanabe

{"title":"Visualizing the Correspondence of Feature Point Mapping between DICOM Images before and after Surgery","authors":"H. Noborio, Shota Uchibori, M. Koeda, Kaoru Watanabe","doi":"10.1145/3340074.3340075","DOIUrl":"https://doi.org/10.1145/3340074.3340075","url":null,"abstract":"We extract feature point mapping between preoperative and postoperative Digital Imaging and Communications in Medicine (DICOM) images from magnetic resonance imaging (MRI) or from computer tomography (CT). The aim is to quantitatively investigate brain shift during intraoperative surgery. First, using 124 two-dimensional images constituting DICOM, a large number of 2D feature points are extracted as uniformly as possible inside the brain. Next, we extract one pair from the 124 preoperative images and the 124 postoperative images and construct map correspondences of similar feature points with a range of DICOM gray values. If the Euclidean distance between the two feature points in the 2D images is too large, the pair of feature points is deleted to prevent mis-mapping; brain shifts are usually less than 2-3 cm. Finally, we find image pairs with the highest number of mappings from DICOM images before and after surgery (two-dimensional stacked three-dimensional images), and generate graph representing correspondences between image pairs with the highest number. Finally, we visualize 3D correspondences between DICOM images before and after surgery.","PeriodicalId":196396,"journal":{"name":"Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130449324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cervical Cancer Risk Prediction Model and Analysis of Risk Factors based on Machine Learning 基于机器学习的宫颈癌风险预测模型及危险因素分析

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 2019-05-29 DOI: 10.1145/3340074.3340078

Wen-shang Yang, X. Gou, Tongqing Xu, Xiping Yi, M. Jiang

引用次数: 14

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology 2019年第十一届生物信息学与生物医学技术国际会议论文集

Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology Pub Date : 1900-01-01 DOI: 10.1145/3340074

引用次数: 0