Filters








16 Hits in 6.9 sec

Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets

Ching-Juei Yang, Chien-Kuo Wang, Yu-Hua Dean Fang, Jing-Yao Wang, Fong-Chin Su, Hong-Ming Tsai, Yih-Jyh Lin, Hung-Wen Tsai, Lee-Ren Yeh, Khanh N.Q. Le
2021 PLoS ONE  
The aim of the study was to use a previously proposed mask region–based convolutional neural network (Mask R-CNN) for automatic abnormal liver density detection and segmentation based on hepatocellular  ...  This study trained a Mask R-CNN on various HCC images to construct a medical model that serves as an auxiliary tool for alerting radiologists to abnormal CT density in liver scans; this model can simultaneously  ...  We also thank Miss Tzu-Shan Chen from the Department of Medical Research, E-Da Hospital in Taiwan, for assistance in statistical analysis.  ... 
doi:10.1371/journal.pone.0255605 pmid:34375365 pmcid:PMC8354440 fatcat:tkk5dd3llbetngqaep3tmn3xsu

Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images [article]

Sang-gil Lee, Eunji Kim, Jae Seok Bae, Jung Hoon Kim, Sungroh Yoon
2021 arXiv   pre-print
misaligned multiphase CT images.  ...  By introducing an attention-guided multiphase alignment in feature space, this study presents a fully automated, end-to-end learning framework for detecting FLLs from multiphase computed tomography (CT  ...  In [15] , a multiphase variant tailored to liver lesion detection is proposed by administering a grouped convolution [19] to the base CNN and introducing 1 × 1 convolutions on top of the grouped feature  ... 
arXiv:2112.01535v1 fatcat:oogqdulohnf6liwlnwygmvokai

Advances in Deep Learning-Based Medical Image Analysis

Xiaoqing Liu, Kunlun Gao, Bo Liu, Chengwei Pan, Kongming Liang, Lifeng Yan, Jiechao Ma, Fujin He, Shu Zhang, Siyuan Pan, Yizhou Yu
2021 Health Data Science  
This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications.  ...  With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active  ...  Acknowledgments This study was supported in part by grants from the Zhejiang Provincial Key Research & Development Program (No. 2020C03073).  ... 
doi:10.34133/2021/8786793 fatcat:d6nkb4yoxrcgni4y5owju5pnh4

Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning [article]

Yuankai Huo, Jinzheng Cai, Chi-Tung Cheng, Ashwin Raju, Ke Yan, Bennett A. Landman, Jing Xiao, Le Lu, Chien-Hung Liao, Adam P. Harrison
2020 arXiv   pre-print
, in differentiating four major liver lesion types.  ...  for more precise characterization of liver lesions; (3) using a semi-automatic process, we bootstrap off of 200 gold standard annotations to curate another 1001 patients.  ...  To fit CT volumes into GPU memory, we first apply a robust multi-phase liver segmentation model [?] , crop around the resulting mask, and then resample the CT subvolume into 176×256×48. B.  ... 
arXiv:2006.15691v2 fatcat:rqgpqv46ingoncrmvatpilhv7i

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

Anna Castaldo, Davide Raffaele De Lucia, Giuseppe Pontillo, Marco Gatti, Sirio Cocozza, Lorenzo Ugga, Renato Cuocolo
2021 Diagnostics  
In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications.  ...  While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy.  ...  This solution automatically detects the contours of liver in PET images. In Table 1 , a summary of the reviewed papers is presented.  ... 
doi:10.3390/diagnostics11071194 fatcat:zmwt7urwinac7bshxcxm6q7doe

A Machine Learning Model to Predict Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization

Ali Morshid, Khaled M. Elsayes, Ahmed M. Khalaf, Mohab M. Elmohr, Justin Yu, Ahmed O. Kaseb, Manal Hassan, Armeen Mahvash, Zhihui Wang, John D. Hazle, David Fuentes
2019 Radiology: Artificial Intelligence  
Shape image features of the tumor and background liver were the dominant features correlated to the TTP as selected by the Boruta method and were used to predict the outcome.  ...  This preliminary study demonstrates that quantitative image features obtained prior to therapy can improve the accuracy of predicting response of HCC to TACE.  ...  Acknowledgments This work was supported in part by the _____.  ... 
doi:10.1148/ryai.2019180021 pmid:31858078 pmcid:PMC6920060 fatcat:sn6vvdhaanhbhcviqpw5z4nxaa

PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images [article]

Yingying Xu, Ming Cai, Lanfen Lin, Yue Zhang, Hongjie Hu, Zhiyi Peng, Qiaowei Zhang, Qingqing Chen, Xiongwei Mao, Yutaro Iwamoto, Xian-Hua Han, Yen-Wei Chen (+1 others)
2021 arXiv   pre-print
In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally  ...  exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase.  ...  The public datasets of liver tumor only provide single-phase images of PV phase. The study of liver tumor segmentation based on multi-phase CT images is restricted by dataset acquisition.  ... 
arXiv:2103.00274v1 fatcat:5p4asf4l7vafpjhxj4ywzqu2ku

Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges

Tanzila Saba
2020 Journal of Infection and Public Health  
Cancer also known as tumor must be quickly and correctly detected in the initial stage to identify what might be beneficial for its cure.  ...  Cancer is a fatal illness often caused by genetic disorder aggregation and a variety of pathological changes.  ...  Acknowledgements This work was supported by the research Project [Brain Tumor Detection and Classification using 3D CNN and Feature Selection Architecture]; Prince Sultan University; Saudi Arabia [SEED-CCIS  ... 
doi:10.1016/j.jiph.2020.06.033 pmid:32758393 fatcat:sglazth4znh5jjtozguaktruce

Automated segmentation of liver segment on portal venous phase MR images using a 3D convolutional neural network

Xinjun Han, Xinru Wu, Shuhui Wang, Lixue Xu, Hui Xu, Dandan Zheng, Niange Yu, Yanjie Hong, Zhixuan Yu, Dawei Yang, Zhenghan Yang
2022 Insights into Imaging  
Objective We aim to develop and validate a three-dimensional convolutional neural network (3D-CNN) model for automatic liver segment segmentation on MRI images.  ...  In indirect evaluation, 93.4% (99/106) of lesions could be assigned to the correct segment by only referring to the results from automated segmentation.  ...  Ning Guo for constructive comments and critical revision of the manuscript. We thank Xianjun Han, Hao Ren, and Boyang Ma, Yaosong Zhou and Xiaolan Zhang for the manual segmentation work in this study.  ... 
doi:10.1186/s13244-022-01163-1 pmid:35201517 pmcid:PMC8873293 fatcat:nhp5votwtnhepanu6zuwqy7oiq

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
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with  ...  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  ...  Annotations of the liver and tumors were performed by radiologists.  ... 
arXiv:2010.14808v2 fatcat:hsfrknwdlffovdtqyuoi5cp24a

Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning

Julie Wang, Alexander Wood, Chao Gao, Kayvan Najarian, Jonathan Gryak
2021 Entropy  
Computed tomography (CT) is the imaging modality of choice to assess patients with blunt spleen trauma, which may include lacerations, subcapsular or parenchymal hematomas, active hemorrhage, and vascular  ...  While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is  ...  of intracranial hemorrhage [27, 28] , lung cancer [29] , as well as liver and brain tumors [30] .  ... 
doi:10.3390/e23040382 pmid:33804831 fatcat:lol7wa6m4jhplp5lccdsiz72zm

ESGAR 2017 Book of Abstracts

2017 Insights into Imaging  
Conclusion: Automatic segmentation of pancreatic cancer is feasible using texture features from b1000 diffusion images in combination with a random forest model.  ...  Conclusion: Automatic segmentation using an SLT is able to reproduce manual expert segmentations with an AUC of >0.90, suggesting that it can be a time-ef cient solution to help delineation in daily practice  ... 
doi:10.1007/s13244-017-0557-2 pmid:28534156 pmcid:PMC5440311 fatcat:iz2fonoizvcoxm65237mvsxajy

A Review of Deep Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Jae Sung Lee
2020 IEEE Transactions on Radiation and Plasma Medical Sciences  
PET AC based on computed tomography (CT) frequently results in artifacts in attenuationcorrected PET images, and these artifacts mainly originate from CT artifacts and PET-CT mismatches.  ...  In this article, a review is presented on the limitations of the PET AC in current dual-modality PET/CT and PET/MRI scanners, in addition to the current status and progress of DL-based approaches, for  ...  An et al. improved the UTE-based AC by the application of a multiphase levelset algorithm for the UTE MRI segmentation, in which the intensity inhomogeneity correction was incorporated.  ... 
doi:10.1109/trpms.2020.3009269 fatcat:c5eizqypcrh3vbnoadglp426fa

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review [article]

Juan J. Cerrolaza, Mirella Lopez-Picazo, Ludovic Humbert, Yoshinobu Sato, Daniel Rueckert, Miguel Angel Gonzalez Ballester, Marius George Linguraru
2018 arXiv   pre-print
analysis to increase the impact of 35 medical imaging applications on the future of healthcare.  ...  The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods.  ...  Acknowledgements This paper was supported in part by the Marie Skodoska-Curie Actions of the UE Framework Program for Research and Innovation, under REA grant agreement 706372.  ... 
arXiv:1812.08577v1 fatcat:xjw2g25pxfftpnduss6d5sggzu

Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images

Sang-gil Lee, Eunji Kim, Jae Seok Bae, Jung Hoon Kim, Sungroh Yoon
2021
misaligned multiphase CT images.  ...  By introducing an attention-guided multiphase alignment in feature space, this study presents a fully automated, end-to-end learning framework for detecting FLLs from multiphase computed tomography (CT  ...  Visualization of registration of multiphase CT dataset. CT images and segmentation masks are registered using the non-rigid method from SimpleElastix with default parameters.  ... 
doi:10.48550/arxiv.2112.01535 fatcat:vzw5yembvrcbbe2fodlwpex35i
« Previous Showing results 1 — 15 out of 16 results