Predicting Scores of Medical Imaging Segmentation Methods with Meta-learning

Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and segmentation methods utilized image processing for cancer diseases for most body's sections are clearly reviewed.

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Domain Generalizer: A Few-Shot Meta Learning Framework for Domain Generalization in Medical Imaging

Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning - Lecture Notes in Computer Science ◽

10.1007/978-3-030-60548-3_8 ◽

2020 ◽

pp. 73-84

Author(s):

Pulkit Khandelwal ◽

Paul Yushkevich

Keyword(s):

Medical Imaging ◽

Learning Framework ◽

Meta Learning

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Three-level evaluation process for segmentation methods in medical imaging

10.1117/12.467169 ◽

2002 ◽

Cited By ~ 1

Author(s):

Regina Pohle ◽

Klaus D. Toennies

Keyword(s):

Medical Imaging ◽

Evaluation Process ◽

Segmentation Methods

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Extracting Retinal Anatomy and Pathological Structure Using Multiscale Segmentation

Applied Sciences ◽

10.3390/app9183669 ◽

2019 ◽

Vol 9 (18) ◽

pp. 3669 ◽

Cited By ~ 1

Author(s):

Geng ◽

Che ◽

Xiao ◽

Liu

Keyword(s):

Image Segmentation ◽

Medical Imaging ◽

Blood Vessels ◽

General Applicability ◽

Learning Techniques ◽

Large Databases ◽

Segmentation Methods ◽

Fundus Photographs ◽

Optic Discs ◽

Pathological Structure

Fundus image segmentation technology has always been an important tool in the medical imaging field. Recent studies have validated that deep learning techniques can effectively segment retinal anatomy and determine pathological structure in retinal fundus photographs. However, several groups of image segmentation methods used in medical imaging only provide a single retinopathic feature (e.g., roth spots and exudates). In this paper, we propose a more accurate and clinically oriented framework for the segmentation of fundus images from end-to-end input. We design a four-path multiscale input network structure that learns network features and finds overall characteristics via our network. Our network’s structure is not limited by segmentation of single retinopathic features. Our method is suitable for exudates, roth spots, blood vessels, and optic discs segmentation. The structure has general applicability to many fundus models; therefore, we use our own dataset for training. In cooperation with hospitals and board-certified ophthalmologists, the proposed framework is validated on retinal images from large databases and can improve diagnostic performance compared to state-of-the-art methods that use smaller databases for training. The proposed framework detects blood vessels with an accuracy of 0.927, which is comparable to exudate accuracy (0.939) and roth spot accuracy (0.904), providing ophthalmologists with a practical diagnostic and a robust analytical tool.

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Variational computing based segmentation methods for medical imaging by using CNN

2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010) ◽

10.1109/cnna.2010.5430256 ◽

2010 ◽

Cited By ~ 9

Author(s):

Alexandru Gacsadi ◽

Peter Szolgay

Keyword(s):

Medical Imaging ◽

Segmentation Methods

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The Impact of Neural Network Techniques in the Optimization of the Image Processing

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.55.2.20 ◽

2020 ◽

Vol 55 (2) ◽

Author(s):

Dalya Abdullah Anwer

Keyword(s):

Neural Network ◽

Image Processing ◽

Medical Imaging ◽

Threshold Value ◽

World Health ◽

Morphological Operations ◽

Segmentation Methods ◽

The World ◽

Feature Values ◽

The Impact

Nowadays, image processing is widely utilized in many applications and for various purposes. Scholars proposed and suggested various techniques of image processing. The neural network is one of the main processing techniques, which is a state-of-art method. This paper aims to investigate neural network techniques in the field of image processing. Moreover, medical imaging, as well as increasing trends of utilizing digital medical imaging, has gained huge attention in the health sectors. In this regard, this paper focuses on the effect of neural networks in optimizing medical image processing. In this context, the early diagnosis and detection of the eye have an important role in the avoidance of visual impairment, because of the fact that around 45 million people have visual impairments all over the world, according to the World Health Organization. For this reason, the current paper introduces a new method based on image processing for vascular segmentation based on a morphological active contour. Then, segmentation carried out based on morphological operations, fuzzy c-means, and watershed transform. The output of such segmentation methods was given to conventional neural network. The optimized feature values are then extracted. The threshold value is set to compare these optimized feature values. From this, the best segmentation methods will be obtained.

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Coronary Stenosis Measurements Using K-Means Clustering

2018 Design of Medical Devices Conference ◽

10.1115/dmd2018-6968 ◽

2018 ◽

Author(s):

Farhad Akhbardeh ◽

Hasan Demirel

Keyword(s):

Medical Imaging ◽

Medical Science ◽

Region Of Interest ◽

Alternative Methods ◽

Body Images ◽

Manual Inspection ◽

Segmentation Methods ◽

Edge Based ◽

Processing Techniques ◽

Time Frames

Medical imaging is one of common area that nowadays researchers uses human body images for clinical or medical science [1] [2]. Currently most of the diagnoses are performed by doctors after manual inspection of real time frames of the video generated by the respective medical imaging systems. In this paper, we propose to use digital image processing techniques in detection and categorization of the clogs in the arteries (stenosis/blockage) by using the frames generated from the X-ray angiography [3][4]. Utilized image pre-processing methods includes selecting a line of Interest (LOI) on blocked vessel and further selection of the region of interest (ROI) on that area, then automatically cropping the region of interest followed by Gaussian filtering for smoothing. In the post processing, three alternative methods are proposed to measure the stenosis in the vessel. The first method applies thresholding (Local) to extract the vessel of interest. The extracted vessel is analyzed for the calculation of the stenosis in percentage [5]. The second method utilizes segmentation (both edge-based and region-based) of the vessel tissue over the extracted pixels of ROI. The final method uses K-means clustering to differentiate between the vessel regions and non-vessel regions. Among the proposed methods K-means clustering based method outperforms the thresholding and segmentation methods.

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