Proceedings of the 5th International Conference on Biomedical Signal and Image Processing最新文献

{"title":"Nonrigid Registration of Multimodal Images Using Local Structural Descriptors","authors":"Lei Wang, Shen-Wen Huang, Qian Chang, Q. Zheng, Hao Chen","doi":"10.1145/3417519.3417559","DOIUrl":"https://doi.org/10.1145/3417519.3417559","url":null,"abstract":"Nonrigid registration plays an important role in reducing potential intensity and geometric differences across individual images with varying modalities. In this paper, we propose a new registration method by introducing two local structural descriptors and a similarity measure. The descriptors are used to highlight small intensity variations and represent the texture characteristics of desirable objects, while the measure estimates image differences based on the introduced descriptors. With these descriptors and measure, multimodal images can be aligned in the free form deformation (FFD) registration framework. Experimental results on two public magnetic resonance (MR) image datasets demonstrated that the developed method can achieve reasonable registration accuracy, and outperformed some existing methods.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132006666","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}

{"title":"Asymmetric and Square Convolutional Neural Network for SAR Ship Detection from Scratch","authors":"L. Han, Xi Zhao, W. Ye, Da Ran","doi":"10.1145/3417519.3417550","DOIUrl":"https://doi.org/10.1145/3417519.3417550","url":null,"abstract":"Despite there have been little research on synthetic aperture radar (SAR) ship detection from scratch, the transfer learning-based studies are still the mainstream. Due to some limitations of the fixed structures of the backbone, it is hard to improve and optimize these parts of networks and there is between domain mismatch which restricts the performance of the convolutional neural network (CNN) to some extent. Addressing the issue, we design an asymmetric and square convolution block (A-S CB) which is easy to be embedded into any CNNs and helps to significantly reduce the number of parameters and computations without serious damages to detection accuracy. We integrated the proposed A-S CB to the classic SSD, namely A-S SSD, which can be trained from scratch. Experiments on RDISD_SAR proves our approach reaches competitive performance in both accuracy and detection speed compared with the typical DSOD, and in contrast with SSD, it holds some superiorities in aspects of accuracy, speed, number of parameters, and computations.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129823697","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}

{"title":"CNN Assisted Hybrid Algorithm for Medical Images Segmentation","authors":"A. Khvostikov, A. Krylov, I. Mikhailov, P. Malkov","doi":"10.1145/3417519.3417557","DOIUrl":"https://doi.org/10.1145/3417519.3417557","url":null,"abstract":"In this report we focus on a hybrid method based on a convolutional neural network (CNN) for histological image segmentation. We propose a CNN assisted interactive segmentation tool with weakly-supervised learning to accelerate the process of manual image annotation. The core of our annotation approach is a classical KNN classifier that uses parameters predicted by CNN. User annotates an image with scribbles of two types corresponding to glands and non-glands histological structures. Next the model performs label propagation to all unlabeled pixels providing user a fully annotated image build from his scribbled-based input. User can interact with the annotation tool and add new scribbles to correct the result. The algorithm allows to reduce one image annotation time from 150 to 25-30 minutes for PATH-DT-MSU dataset that can potentially seriously increase the number of fully annotated histological images which is necessary for the development of diagnostic algorithms.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133638129","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}

{"title":"Full Tooth Contour Recognition and Model Reconstruction Method Based on CT Images","authors":"Kaile Song, Songhua Ma, Zhaohui Wang","doi":"10.1145/3417519.3417549","DOIUrl":"https://doi.org/10.1145/3417519.3417549","url":null,"abstract":"It is of great significance for the treatment of oral diseases to establish a digital 3d model of teeth based on oral CT images. In order to establish a 3d digital model of teeth by using oral CT images, the contour segmentation, coordinate transformation and surface model reconstruction of oral CT images are required. The structure tensor information can reflect the local structure information of the image by using the multi-channel information, and many models constructed by using the local tensor information have the de-noising effect. Therefore, the area term fused with structural tensor information was added to the (Local Gaussian Distribution Fitting) LGDF model to segment the contour of a single tooth. The segmentation results show that the improved model can be segmented into ideal contours without Interference noise area and false boundary. After the segmentation of all the contours was completed, all the contours were converted from two-dimensional CT coordinates to three-dimensional coordinate forms, and the surface model was obtained by surface reconstruction of three-dimensional scattered point cloud using Crust algorithm. The surface model construction method proposed in this paper has better surface quality.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130543063","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}

{"title":"A Method for Automatic Tracking of Cell Nuclei With Weakly-Supervised Mitosis Detection in 2D Microscopy Image Sequences","authors":"Alexandr Yu. Kondrati'ev, H. Yaginuma, Y. Okada, A. Krylov, D. Sorokin","doi":"10.1145/3417519.3417558","DOIUrl":"https://doi.org/10.1145/3417519.3417558","url":null,"abstract":"Due to a high interest in microscopic cell migration analysis for biological research, numerous cell segmentation and tracking algorithms has emerged. The main tasks of cell tracking methods are to segment each cell and establish individual cell lineages over time, accounting for possible cell disappearance and division events. In some datasets, cells can drastically change their appearance during the mitosis stage, thus making division detection a challenging problem. The most prominent methods exploit different neural network architectures for at least one of these tasks. We propose a method that uses a single UNet topology network to solve both tasks of cell nuclei instance segmentation and detection of mitotic events. For instance segmentation, the network is learned to segment primary masks and object centroids that are used by watershed transform to obtain individual nuclei regions. For mitotic events detection, we first manually mark cells entering and finishing mitosis. Then, previously trained network is used to generate weak nuclei segmentation labels for all data images in sequences with marked mitotic events. We add an additional output to the trained network for segmentation of mitotic events. The training is resumed for both tasks on initial ground truth segmentation, generated weak labels, and crude mitotic events markers. For tracking, we use generalized nearest neighbour method that can greedily search the best 1-to-1 and 1-to-2 instance connections over multiple frames. Segmentation of the mitotic events produced by the trained model is incorporated into the tracking algorithm to improve cell division detection. We evaluate the results of the proposed method and compare it with the previously developed algorithm, achieving better performance on our dataset. We assume it is possible to upgrade other existing segmentation frameworks to also learn the task of segmenting mitotic events and enhance division detection using the proposed pipeline.","PeriodicalId":239684,"journal":{"name":"Proceedings of the 5th International Conference on Biomedical Signal and Image Processing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128057094","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}