2578-160X (Online) Journal Abbreviation: Med. Imag. Proc. Tech. | The purpose of Medical Imaging Process & Technology (MIPT) is to act as a platform for the exchange of research results concerning medical imaging technology in medical detection, prevention, health, etc. Articles published in MIPT will be of interest of medical imaging system and medical image processing, and the scope welcomes the manuscripts from all the medical imaging aspects including imaging diagnostics, radiology, endoscopy, medical thermal imaging technology, medical photography and microscope. The original research is highly regarded, such as visualization, data structure and representation, pattern matching and recognition, object detection, change detection, motion estimation and related methods. |
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| Journal Title | APCs |
|---|---|
| Medical Imaging Process & Technology | $1000 |
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Vol 7, No 1 (2024)
Table of Contents
Open Access
Open Access
Radiomics, a quantitative approach to medical imaging, employs computational methods to extract features from the images, revealing hidden characteristics of specific regions. This emerging field leverages advanced techniques to analyze a spectrum of features from modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) scans, aiming to decode tissue characteristics, disease progression, and treatment responses. The radiomics workflow integrates image acquisition, segmentation, feature selection, and data integration, utilizing advanced techniques such as deep learning, machine learning, and data mining. Radiomics demonstrates considerable potential in cancer detection and management, exhibiting high sensitivity and specificity in distinguishing between benign and malignant tumors and predicting outcomes. However, challenges such as imaging protocol variability, overfitting, and standardization requirements impede its broad clinical adoption. Innovations in multi-modal radiomics, deep learning, and genomics integration strive to mitigate these constraints. This review elucidates radiomics’ capabilities, current applications, benefits, challenges, and future directions in oncology.
Open Access
Advancements in Medical Image Segmentation have revolutionized clinical diagnostics and treatment planning. This review explores a wide range of segmentation techniques applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images, emphasizing their clinical implications and future directions. CT segmentation techniques, including U-Net and its variant nnU-Net, are essential in oncology for precise tumor delineation, in cardiology for coronary artery analysis, and in pulmonology for lung lesion detection. These methods enhance radiotherapy targeting, surgical planning, and overall diagnostic accuracy. The nnU-Net, known for its self-configuring nature, is particularly notable for setting new benchmarks in medical image segmentation tasks. MRI segmentation benefits from superior soft tissue contrast. Techniques like Mask Region-based Convolutional Neural Network (R-CNN) excel in identifying brain lesions, assessing musculoskeletal injuries, and monitoring soft tissue tumors. These methods support detailed visualization of internal structures, improving diagnosis and guiding targeted interventions. U-Net architectures also play a critical role in MRI segmentation, demonstrating high efficacy in various applications such as brain and prostate imaging. A systematic review of the literature reveals performance metrics for various segmentation techniques, such as accuracy, sensitivity, specificity, and processing time. Traditional methods like thresholding and edge detection are contrasted with advanced deep learning and machine learning approaches, highlighting the strengths and limitations of each. The review also addresses methodological approaches, including data collection, analysis, and evaluation metrics. Future prospects include integrating 3D and 4D segmentation, multimodal data fusion, and enhancing AI explain ability. These innovations aim to refine diagnostic processes, personalize treatments, and improve patient outcomes. Clinical applications of these segmentation techniques demonstrate significant advantages in radiology, oncology, and cardiology, though challenges such as data variability and noise persist. Emerging strategies like data augmentation and transfer learning offer potential solutions to these issues. The continuous evolution of medical image segmentation techniques promises to enhance diagnostic accuracy, efficiency, and the personalization of patient care, ultimately leading to better healthcare outcomes.
Announcements
The Conflict of Interest Policy statement |
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Conflicts of interest may exist when professional judgements concerning a primary interest has the possibility of being influenced by a secondary interest (e.g.: financial gains). It is to be noted that even perceptions of conflicts of interest are as important as the actual conflicts of interest. |
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| Posted: 2022-09-13 | More... |
The 33rd Annual ASE Scientific Sessions will be held on 10th -13th June, 2022 |
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 The 33rd Annual ASE Scientific Sessions will be held on 10-13 June, 2022 in Seattle, Washington, USA. The Scientific Sessions is the premier international conference dedicated to the use of cardiovascular ultrasound in clinical patient care. |
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| Posted: 2022-04-10 | More... |
Summary of recommended conferences in medical imaging field |
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In the field of medical imaging, there are several recommended conferences as followed:
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| Posted: 2021-06-26 | More... |
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