Computerized Medical Imaging and Graphics
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#1Arnaud Paris (University of Orléans)H-Index: 1
#2Adel Hafiane (University of Orléans)H-Index: 20
Abstract null null Ultrasound guided regional anesthesia (UGRA) has emerged as a powerful technique for pain management in the operating theatre. It uses ultrasound imaging to visualize anatomical structures, the needle insertion and the delivery of the anesthetic around the targeted nerve block. Detection of the nerves is a difficult task, however, due to the poor quality of the ultrasound images. Recent developments in pattern recognition and machine learning have heightened the need for compu...
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#1Anirudh Ashok Aatresh (KREC: National Institute of Technology, Karnataka)
#2Rohit Prashant Yatgiri (KREC: National Institute of Technology, Karnataka)
Last. Jyoti R. Kini (Manipal University)H-Index: 5
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Abstract null null Image segmentation remains to be one of the most vital tasks in the area of computer vision and more so in the case of medical image processing. Image segmentation quality is the main metric that is often considered with memory and computation efficiency overlooked, limiting the use of power hungry models for practical use. In this paper, we propose a novel framework (Kidney-SegNet) that combines the effectiveness of an attention based encoder-decoder architecture with atrous ...
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#1Hongming Xu (DUT: Dalian University of Technology)H-Index: 8
#2Lina Liu (U of A: University of Alberta)H-Index: 2
Last. Cheng Lu (Case Western Reserve University)H-Index: 24
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Abstract null null While deep learning models have demonstrated outstanding performance in medical image segmentation tasks, histological annotations for training deep learning models are usually challenging to obtain, due to the effort and experience required to carefully delineate tissue structures. In this study, we propose an unsupervised method, termed as tissue cluster level graph cut (TisCut), for segmenting histological images into meaningful compartments (e.g., tumor or non-tumor region...
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#1Zihao Wang (IRIA: French Institute for Research in Computer Science and Automation)H-Index: 2
#2Clair VandersteenH-Index: 5
Last. Hervé Delingette (IRIA: French Institute for Research in Computer Science and Automation)H-Index: 69
view all 7 authors...
Abstract null null Metal Artifacts creates often difficulties for a high quality visual assessment of post-operative imaging in computed tomography (CT). A vast body of methods have been proposed to tackle this issue, but these methods were designed for regular CT scans and their performance is usually insufficient when imaging tiny implants. In the context of post-operative high-resolution CT imaging, we propose a 3D metal artifact reduction algorithm based on a generative adversarial neural ne...
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#1Xingyu Zhao (CAS: Chinese Academy of Sciences)H-Index: 5
#2Xiang WangH-Index: 3
Last. Shiyuan LiuH-Index: 18
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Abstract null null The diagnosis of preoperative lymph node (LN) metastasis is crucial to evaluate possible therapy options for T1 lung adenocarcinoma patients. Radiologists preoperatively diagnose LN metastasis by evaluating signs related to LN metastasis, like spiculation or lobulation of pulmonary nodules in CT images. However, this type of evaluation is subjective and time-consuming, which may result in poor consistency and low efficiency of diagnoses. In this study, a 3D Multi-scale, Multi-...
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#1Mayank Goswami (IITR: Indian Institute of Technology Roorkee)H-Index: 7
Abstract null null Relatively abundant availability of medical imaging data has provided significant support in the development and testing of Neural Network based image processing methods. Clinicians often face issues in selecting suitable image processing algorithm for medical imaging data. A strategy for the selection of a proper model is presented here. The training data set comprises optical coherence tomography (OCT) and angiography (OCT-A) images of 50 mice eyes with more than 100 days fo...
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#1Vineeta Das (IITG: Indian Institute of Technology Guwahati)H-Index: 5
#2Samarendra Dandapat (IITG: Indian Institute of Technology Guwahati)H-Index: 18
Last. Prabin Kumar Bora (IITG: Indian Institute of Technology Guwahati)H-Index: 15
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Abstract null null High-resolution (HR) retinal optical coherence tomography (OCT) images are preferred by the ophthalmologists to diagnose retinal diseases. These images can be obtained by dense scanning of the target retinal region during acquisition. However, a dense scanning increases the image acquisition time and introduces motion artefacts, which corrupt diagnostic information. Therefore, researchers have a growing interest in developing image processing techniques to recover HR images fr...
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#1Chi-Jui Ho (NTU: National Taiwan University)
Last. Homer H. ChenH-Index: 36
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Whole brain segmentation is an important neuroimaging task that segments the whole brain volume into anatomically labeled regions-of-interest. Convolutional neural networks have demonstrated good performance in this task. Existing solutions, usually segment the brain image by classifying the voxels, or labeling the slices or the sub-volumes separately. Their representation learning is based on parts of the whole volume whereas their labeling result is produced by aggregation of partial segmentat...
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#1Farhan Mohammed (UTS: University of Technology, Sydney)H-Index: 5
#2Xiangjian He (UTS: University of Technology, Sydney)H-Index: 33
Last. Yiguang Lin (UTS: University of Technology, Sydney)H-Index: 14
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Top fields of study
Radiology
Pattern recognition
Computer vision
Computer science
Medicine
Segmentation