468 Hits in 10.3 sec

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
for Histopathology 351 Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-Phase CT Images 352 Domain and Geometry Agnostic CNNs for Left Atrium Segmentation  ...  with Cascaded Convolutional Neural Networks 306 A Weakly-Supervised Learning-Based Feature Localization in Confocal Laser Endomicroscopy Glioma Images 310 A novel mixed reality navigation for laparoscopic  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem? [article]

Jun Ma, Yao Zhang, Song Gu, Cheng Zhu, Cheng Ge, Yichi Zhang, Xingle An, Congcong Wang, Qiyuan Wang, Xin Liu, Shucheng Cao, Qi Zhang (+5 others)
2021 arXiv   pre-print
This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and  ...  Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability  ...  For organ segmentation, in [68] , a classification forest-based weakly supervised organ segmentation method was proposed for livers, spleens and kidneys, where the labels are scribbles on organs.  ... 
arXiv:2010.14808v2 fatcat:hsfrknwdlffovdtqyuoi5cp24a

Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear [chapter]

Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant
2018 Lecture Notes in Computer Science  
Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-Phase CT Images Liang Dong; Lanfen Lin*; Hongjie Hu; Qiaowei Zhang; Qingqing Chen; Yutaro Iwamoto; Xian-Hua Han; Yen-Wei  ...  Daniel Chang; Lei Xing T-43 Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector Sang-gil Lee; Jae Seok Bae; Hyunjae Kim; Jung Hoon Kim; Sungroh  ...  T-129 Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training Wen  ... 
doi:10.1007/978-3-030-00928-1_1 fatcat:ypoj3zplm5awljf6u5c2spgiea

Front Matter: Volume 12032

Ivana Išgum, Olivier Colliot
2022 Medical Imaging 2022: Image Processing  
using a Base 36 numbering system employing both numerals and letters.  ...  SPIE uses a seven-digit CID article numbering system structured as follows:  The first five digits correspond to the SPIE volume number.  The last two digits indicate publication order within the volume  ...  [12032-59] 1R Generative adversarial network for coronary artery plaque synthesis in coronary CT angiography [12032-60] 1S A deep learning [12032-51] 1J A deep kernel method for PET image reconstruction  ... 
doi:10.1117/12.2638192 fatcat:ikfgnjefaba2tpiamxoftyi6sa

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He, Paul Kennedy
2019 Journal of digital imaging  
In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation.  ...  Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation.  ...  In [16] , Christ et al. performed liver segmentation by cascading two FCNs, where the first FCN performed the liver segmentation as the ROI for the second FCN which focused on segmenting the liver lesions  ... 
doi:10.1007/s10278-019-00227-x pmid:31144149 pmcid:PMC6646484 fatcat:bupmhghuxvgthfo2fl7bkkry4a

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.  ...  We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area.  ...  ArXiv was searched for papers mentioning one of a set of terms related to medical imaging.  ... 
doi:10.1016/ pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis [article]

Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu
2020 arXiv   pre-print
Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area.  ...  In this survey, we summarize the current progress on integrating medical domain knowledge into deep learning models for various tasks, such as disease diagnosis, lesion, organ and abnormality detection  ...  In addition, PET images are used to help the lesion detection in CT scans of liver [152] .  ... 
arXiv:2004.12150v3 fatcat:2cqumcjkizgivmo67reznxacie

Deep Semantic Segmentation of Natural and Medical Images: A Review [article]

Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Paul Cohen, Julien Cohen-Adad, Ghassan Hamarneh
2020 arXiv   pre-print
sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups.  ...  In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics.  ...  microscopy images, liver segmentation in abdominal CT scans, and polyp segmentation in colonoscopy videos.  ... 
arXiv:1910.07655v3 fatcat:uxrrmb3jofcsvnkfkuhfwi62yq

Deep Learning Based Pain Treatment

Tarun Jaiswal, Sushma Jaiswal
2019 International Journal of Trend in Scientific Research and Development  
Indeed, the application of machine learning for pain investigationassociated non-imaging problems has been mentioned in publications in scientific journals since 1940-2018.  ...  Among machine learning methods, a subset has so far been applied to pain research-related problems, SVMs, regression models, deep learning and several kinds of neural networks so far most often revealed  ...  This paper presents a method to automatically segment liver and lesions in CT abdomen images using cascaded fully convolutional neural networks (CFCNs) and dense 3D conditional random fields (CRFs A 3D  ... 
doi:10.31142/ijtsrd23639 fatcat:tqg4u3tkgjhmjpya67g3lnewwu

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

Muhammad Zubair Khan, Mohan Kumar Gajendran, Yugyung Lee, Muazzam A. Khan
2021 IEEE Access  
In [183] , the authors proposed an attention-based U-Net model for segmenting CT-150 and CT-82 datasets with various settings.  ...  Each subject includes images from a clinical thoracic CT scan and an XML file that records the results of a two-phase image annotation performed by four radiologists.  ... 
doi:10.1109/access.2021.3086530 fatcat:hacpqwdxybh63j5ygebqszm7qq

Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation [article]

Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Lei Xing, Pheng-Ann Heng
2020 arXiv   pre-print
In this paper, we present a novel semi-supervised method for medical image segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and  ...  a regularization loss for both labeled and unlabeled data.  ...  [50] presented a 2D-3D hybrid architecture for liver and tumor segmentation from CT images.  ... 
arXiv:1903.00348v3 fatcat:tazv622govdfvmjan7ido5k5ce

Modality specific U-Net variants for biomedical image segmentation: A survey [article]

Narinder Singh Punn, Sonali Agarwal
2022 arXiv   pre-print
With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical  ...  In recent studies, U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems for early diagnosis and treatment  ...  Acknowledgment We thank our institute, Indian Institute of Information Technology Allahabad (IIITA), India and Big Data Analytics (BDA) lab for allocating the necessary  ... 
arXiv:2107.04537v4 fatcat:m5oqea5q6vhbhkerjmejder3hu

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation [article]

Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, Xiaowei Ding
2020 arXiv   pre-print
The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks.  ...  However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire.  ...  Other 3D FC-CRF endeavors include a U-net + 3D FC-CRF by Christ et al. (2016) for liver and lesion segmentation in CT images and a 3D FC-CRF with spectral coordinates characterization by Wachinger et  ... 
arXiv:1908.10454v2 fatcat:mjvfbhx75bdkbheysq3r7wmhdi

Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods [article]

Zohaib Salahuddin, Henry C Woodruff, Avishek Chatterjee, Philippe Lambin
2021 arXiv   pre-print
Artificial Intelligence has emerged as a useful aid in numerous clinical applications for diagnosis and treatment decisions.  ...  Finally we discuss limitations, provide guidelines for using interpretability methods and future directions concerning the interpretability of deep neural networks for medical imaging analysis.  ...  [77] proposed Guided Attention Inference Network (GAIN) for generating attention maps for weakly supervised tasks by incorporating attention mining loss and a method for improving these attention maps  ... 
arXiv:2111.02398v1 fatcat:glrfdkbcqrbqto2nrl7dnlg3gq

Deep Learning in Medical Image Registration: A Review [article]

Yabo Fu, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang
2019 arXiv   pre-print
This paper presents a review of deep learning (DL) based medical image registration methods.  ...  We provided a comprehensive comparison among DL-based methods for lung and brain deformable registration using benchmark datasets.  ...  Award, a philanthropic award provided by the Winship Cancer Institute of Emory University.  ... 
arXiv:1912.12318v1 fatcat:kuvckosqd5hp7asg6dofhuiis4
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