A5091855300- Medical Image Segmentation Techniques
- Radiomics and Machine Learning in Medical Imaging
- Surgical Simulation and Training
- Medical Imaging and Analysis
- Advanced Neural Network Applications
- Robotics and Sensor-Based Localization
- Anatomy and Medical Technology
- Hepatocellular Carcinoma Treatment and Prognosis
- Advanced X-ray and CT Imaging
- AI in cancer detection
- Brain Tumor Detection and Classification
- Advanced Vision and Imaging
- Pancreatic and Hepatic Oncology Research
- Medical Imaging Techniques and Applications
- Functional Brain Connectivity Studies
- EEG and Brain-Computer Interfaces
- 3D Shape Modeling and Analysis
- Artificial Intelligence in Healthcare and Education
- 3D Surveying and Cultural Heritage
- Advanced Image and Video Retrieval Techniques
- Image Processing Techniques and Applications
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Advanced Numerical Analysis Techniques
- Cardiac, Anesthesia and Surgical Outcomes
- Advanced Neuroimaging Techniques and Applications
Shenzhen Institutes of Advanced Technology
2015-2024
Chinese Academy of Sciences
2015-2024
University of Chinese Academy of Sciences
2015-2024
Weatherford College
2023
Guangzhou Experimental Station
2023
National Natural Science Foundation of China
2022
Southern Medical University
2022
Institute of Automation
2022
Zhujiang Hospital
2022
Zhongshan Hospital
2020
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far most widely investigated medical processing task, but various segmentation typically been organized in isolation, such that algorithm development was driven by need to tackle single clinical problem. We hypothesized method capable performing well on multiple tasks will generalize previously unseen task and potentially outperform...
In this work, we report the set-up and results of Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Conferences Medical Image Computing Computer-Assisted Intervention (MICCAI) 2018. The image dataset is diverse contains primary secondary tumors varied sizes appearances various lesion-to-background levels (hyper-/hypo-dense), created collaboration seven hospitals research institutions. Seventy-five...
Liver tumors segmentation from computed tomography (CT) images is an essential task for diagnosis and treatments of liver cancer. However, it difficult owing to the variability appearances, fuzzy boundaries, heterogeneous densities, shapes sizes lesions. In this paper, automatic method based on convolutional neural networks (CNNs) presented segment lesions CT images. The CNNs one deep learning models with some filters which can learn hierarchical features data. We compared model popular...
In this work, we report the set-up and results of Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Conferences Medical Image Computing Computer-Assisted Intervention (MICCAI) 2018. The image dataset is diverse contains primary secondary tumors varied sizes appearances various lesion-to-background levels (hyper-/hypo-dense), created collaboration seven hospitals research institutions. Seventy-five...
Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase safety operation through context-sensitive warnings semi-autonomous robotic or improve training surgeons via data-driven feedback. In up to 91% average precision has been reported phase recognition on an open data single-center video dataset. this work we investigated generalizability algorithms in a multicenter setting including more...
Variations in the shape and appearance of anatomical structures medical images are often relevant radiological signs disease. Automatic tools can help automate parts this manual process. A cloud-based evaluation framework is presented paper including results benchmarking current state-of-the-art imaging algorithms for structure segmentation landmark detection: VISCERAL Anatomy benchmarks. The implemented virtual machines cloud where participants only access training data be run privately by...
An adaptively regularized kernel-based fuzzy<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>C</mml:mi></mml:mrow></mml:math>-means clustering framework is proposed for segmentation of brain magnetic resonance images. The can be in the form three algorithms local average grayscale being replaced by filter, median and devised weighted images, respectively. employ heterogeneity grayscales neighborhood exploit this measure contextual information replace...
Abstract Although the middle temporal gyrus (MTG) has been parcellated into subregions with distinguished anatomical connectivity patterns, whether structural topography of MTG can inform functional segregations this area remains largely unknown. Accumulating evidence suggests that brain's underlying organization and function be directly effectively delineated resting‐state (RSFC) by identifying putative boundaries between cortical areas. Here, RSFC profiles were used to explore defined four...
The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China. task to perform dense depth estimation using 7 training datasets 2 test sets structured light captured porcine cadavers. These were provided by a team Intuitive Surgical. 10 teams participated day. This paper contains 3 additional methods which submitted after finished as well supplemental section from these on issues they found with dataset.
Objectives: To characterize the brain activation patterns evoked by manual and electroacupuncture on normal human subjects. Design: We used functional magnetic resonance imaging (fMRI) to investigate regions involved in acupuncture needle stimulation. A block design was adopted for study. Each run consists of 5 minutes, starting with 1-minute baseline two stimulation, interval between stimuli 1 minute. Four runs were performed each subject, acupuncture. The order modalities randomized among...
Accurate determination of intrahepatic anatomy remains challenging for laparoscopic anatomical hepatectomy (LAH). Laparoscopic augmented reality navigation (LARN) is expected to facilitate LAH primary liver cancer (PLC) by identifying the exact location tumors and vessels. The study was evaluate safety effectiveness our independently developed LARN system in PLC.From May 2018 July 2020, included 85 PLC patients who underwent three-dimensional (3D) LAH. According whether performed during...
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems. Nowadays, learning-based approaches outperform classical methods, relying, however, on large, annotated datasets. Furthermore, algorithms often trained make predictions isolation each other, without exploiting potential cross-task relationships. With the EndoVis 2022 SAR-RARP50 challenge, we...
The objective of the study was to develop and validate a radiomics-based formula for preoperative prediction postoperative pancreatic fistula (POPF) in patients undergoing pancreaticoduodenectomy (PD).A total 117 consecutive who underwent PD were enrolled this retrospective study. Radiomics features extracted from portal venous phase computed tomography above patients. least absolute shrinkage selection operator logistic regression used construct Rad-score calculation. Then performance...
Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed the diagnosis treatment of disorders. Until now, high variability in shape, local image artifacts, presence tumors have complicated development automatic 3D segmentation. In this study, an three-level AdaBoost-guided active shape model (ASM) proposed segmentation based on enhanced computed tomography images a robust fast manner, with emphasis detection tumors. Methods: The AdaBoost voxel classifier...