2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)最新文献_第3页

Combining fully convolutional networks and graph-based approach for automated segmentation of cervical cell nuclei 结合全卷积网络和基于图的方法实现宫颈细胞核的自动分割

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950548

Ling Zhang, M. Sonka, Le Lu, R. Summers, Jianhua Yao

{"title":"Combining fully convolutional networks and graph-based approach for automated segmentation of cervical cell nuclei","authors":"Ling Zhang, M. Sonka, Le Lu, R. Summers, Jianhua Yao","doi":"10.1109/ISBI.2017.7950548","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950548","url":null,"abstract":"Cervical nuclei carry substantial diagnostic information for cervical cancer. Therefore, in automation-assisted reading of cervical cytology, automated and accurate segmentation of nuclei is essential. This paper proposes a novel approach for segmentation of cervical nuclei that combines fully convolutional networks (FCN) and graph-based approach (FCNG). FCN is trained to learn the nucleus high-level features to generate a nucleus label mask and a nucleus probabilistic map. The mask is used to construct a graph by image transforming. The map is formulated into the graph cost function in addition to the properties of the nucleus border and nucleus region. The prior constraints regarding the context of nucleus-cytoplasm position are also utilized to modify the local cost functions. The globally optimal path in the constructed graph is identified by dynamic programming. Validation of our method was performed on cell nuclei from Herlev Pap smear dataset. Our method shows a Zijdenbos similarity index (ZSI) of 0.92 ± 0.09, compared to the best state-of-the-art approach of 0.89 ± 0.15. The nucleus areas measured by our method correlated strongly with the independent standard (r2 = 0.91).","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90448848","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: 55

Hybrid dermoscopy image classification framework based on deep convolutional neural network and Fisher vector 基于深度卷积神经网络和Fisher向量的混合皮肤镜图像分类框架

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950524

Zhen Yu, Dong Ni, Siping Chen, Jin Qin, Shengli Li, Tianfu Wang, B. Lei

{"title":"Hybrid dermoscopy image classification framework based on deep convolutional neural network and Fisher vector","authors":"Zhen Yu, Dong Ni, Siping Chen, Jin Qin, Shengli Li, Tianfu Wang, B. Lei","doi":"10.1109/ISBI.2017.7950524","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950524","url":null,"abstract":"Dermoscopy image is usually used in early diagnosis of malignant melanoma. The diagnosis accuracy by visual inspection is highly relied on the dermatologist's clinical experience. Due to the inaccuracy, subjectivity, and poor reproducibility of human judgement, an automatic recognition algorithm of dermoscopy image is highly desired. In this work, we present a hybrid classification framework for dermoscopy image assessment by combining deep convolutional neural network (CNN), Fisher vector (FV) and support vector machine (SVM). Specifically, the deep representations of subimages at various locations of a rescaled dermoscopy image are first extracted via a natural image dataset pre-trained on CNN. Then we adopt an orderless visual statistics based FV encoding methods to aggregate these features to build more invariant representations. Finally, the FV encoded representations are classified for diagnosis using a linear SVM. Compared with traditional low-level visual features based recognition approaches, our scheme is simpler and requires no complex preprocessing. Furthermore, the orderless representations are less sensitive to geometric deformation. We evaluate our proposed method on the ISBI 2016 Skin lesion challenge dataset and promising results are obtained. Also, we achieve consistent improvement in accuracy even without fine-tuning the CNN.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78723173","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: 43

Tree-loss function for training neural networks on weakly-labelled datasets 弱标记数据集上训练神经网络的树损失函数

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950521

S. Demyanov, R. Chakravorty, ZongYuan Ge, SeyedBehzad Bozorgtabar, M. Pablo, Adrian Bowling, R. Garnavi

{"title":"Tree-loss function for training neural networks on weakly-labelled datasets","authors":"S. Demyanov, R. Chakravorty, ZongYuan Ge, SeyedBehzad Bozorgtabar, M. Pablo, Adrian Bowling, R. Garnavi","doi":"10.1109/ISBI.2017.7950521","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950521","url":null,"abstract":"Neural networks are powerful tools for medical image classification and segmentation. However, existing network structures and training procedures assume that the output classes are mutually exclusive and equally important. Many datasets of medical images do not satisfy these conditions. For example, some skin disease datasets have images labelled as coarse-grained class (such as Benign) in addition to images with fine-grained labels (such as a Benign subclass called Blue Nevus), and conventional neural network can not leverage such additional data for training. Also, in the clinical decision making, some classes (such as skin cancer or Melanoma) often carry more importance than other lesion types. We propose a novel Tree-Loss function for training and fine-tuning a neural network classifier using all available labelled images. The key step is the definition of the class taxonomy tree, which is used to describe the relations between labels. The tree can be also adjusted to reflect the desired importance of each class. These steps can be performed by a domain expert without detailed knowledge of machine learning techniques. The experiments demonstrate the improved performance compared with the conventional approach even without using additional data.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75228452","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: 5

Phase retrieval in 3D X-ray magnified phase nano CT: Imaging bone tissue at the nanoscale 三维x射线放大相位纳米CT的相位恢复:在纳米尺度上成像骨组织

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950467

Boliang Yu, L. Weber, A. Pacureanu, M. Langer, C. Olivier, P. Cloetens, F. Peyrin

引用次数: 9

In silico model to simulate the radiation response at various fractionation from histopathological images of prostate tumors 用计算机模型模拟前列腺肿瘤组织病理图像中不同部位的辐射反应

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950643

V. Aubert, O. Acosta, N. Rioux-Leclercq, R. Mathieu, F. Commandeur, R. Crevoisier

引用次数: 2

Parameter selection for optimized non-local means filtering of task fMRI 任务fMRI非局部均值滤波优化参数选择

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950564

Jian Li, R. Leahy

引用次数: 7

Design of a low cost ultrasound system using diverging beams and synthetic aperture approach: Preliminary study 采用发散光束和合成孔径方法的低成本超声系统的设计:初步研究

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950710

B. Lokesh, A. Thittai

引用次数: 4

Exploring texture Transfer Learning for Colonic Polyp Classification via Convolutional Neural Networks 基于卷积神经网络的结肠息肉分类纹理迁移学习研究

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950695

E. Ribeiro, M. Häfner, Georg Wimmer, Toru Tamaki, J. Tischendorf, S. Yoshida, Shinji Tanaka, A. Uhl

引用次数: 24

Automated connectivity-based groupwise cortical atlas generation: Application to data of neurosurgical patients with brain tumors for cortical parcellation prediction 基于自动连接的分组皮质图谱生成:应用于脑肿瘤神经外科患者的皮质包裹性预测数据

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950633

Fan Zhang, Pegah Kahali, Yannick Suter, I. Norton, Laura Rigolo, P. Savadjiev, Yang Song, Y. Rathi, Weidong (Tom) Cai, W. Wells, A. Golby, L. O’Donnell

{"title":"Automated connectivity-based groupwise cortical atlas generation: Application to data of neurosurgical patients with brain tumors for cortical parcellation prediction","authors":"Fan Zhang, Pegah Kahali, Yannick Suter, I. Norton, Laura Rigolo, P. Savadjiev, Yang Song, Y. Rathi, Weidong (Tom) Cai, W. Wells, A. Golby, L. O’Donnell","doi":"10.1109/ISBI.2017.7950633","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950633","url":null,"abstract":"This work presents an initial exploration of joint cortical surface and diffusion MRI analysis for neurosurgical patient data. We propose a groupwise cortical modeling strategy that performs an embedding of cortical points from a healthy population and a method for transferring the embedding (with associated information of anatomical label) to patient datasets for cortical parcellation prediction. Our proposed method correlates cortical surfaces based on groupwise white matter connectivity characteristics via a fiber clustering scheme. Unlike other parcellation methods, correspondence of cortical surface vertices is not required. Thus the proposed method can be applied to datasets of patients with brain tumors, using an approximate cortical surface such as a white matter/gray matter boundary derived from diffusion anisotropy. Our initial results on patient data showed good overlap of functional ground truth (subject-specific functional MRI activation areas) with predicted cortical parcels, with 10 of 13 activations overlapping an anatomically corresponding prediction.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82004891","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: 9

Assessing the feasibility of estimating axon diameter using diffusion models and machine learning 评估使用扩散模型和机器学习估计轴突直径的可行性

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950631

Rutger Fick, N. Sepasian, M. Pizzolato, A. Ianuş, R. Deriche

引用次数: 6