Medical Image Segmentation with Deep Learning

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images is time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images have been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and spark research interests in medical image segmentation using deep learning. We propose two convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published

Segmentasi luka diabetes menggunakan algoritma contour image processing

Pengukuran luas luka pada penderita diabetes masih menggunakan cara manual dengan penggaris luka. Sedangkan penggaris yang ditempelkan keluka akan menjadi contaminated agent yang dapat menularkan infeksi pada penderita lain. Metode pengukuran digital diperlukan agar masalah tersebut bisa terselesaikan. Tetapi untuk memperjelas batas antara luka dan kulit diperlukan ketelitian dan akurasi yang tinggi. Untuk itu diperlukan metode pencitraan yang dapat melakukan segmentasi antara batas luka dan kulit paada pasien diabetes berbasis digital yang dinamakan digital planimetry. Penelitian ini menggunakan algoritma contour image processing dari nilai hue, saturation, value (HSV).  kemudian melakukan iterasi sebanyak 5 kali dan filter gamma. Sehingga mendapatkan hasil segmentasi luka. Kesimpulan akhir dari penelitian ini adalah segementasi dengan metode ini belum dapat melakukan segementasi luka dengan baik dan diperlukan tambahan nilai masking yang lebih luas, akan tetapi hasil iterasi ke 5 mendapatkan error terkecil yaitu 0.002%.  Pencitraan digital yang dilakukan dalam penelitian ini dapat dikembangkan untuk menjadi alat ukur luas luka pasien diabetes berbasis digital

Unveiling the role of artificial intelligence for wound assessment and wound healing prediction

Wound healing is a very dynamic and complex process as it involves the patient, wound-level parameters, as well as biological, environmental, and socioeconomic factors. Its process includes hemostasis, inflammation, proliferation, and remodeling. Evaluation of wound components such as angiogenesis, inflammation, restoration of connective tissue matrix, wound contraction, remodeling, and re-epithelization would detail the healing process. Understanding key mechanisms in the healing process is critical to wound research. Elucidating its healing complexity would enable control and optimize the processes for achieving faster healing, preventing wound complications, and undesired outcomes such as infection, periwound dermatitis and edema, hematomas, dehiscence, maceration, or scarring. Wound assessment is an essential step for selecting an appropriate treatment and evaluating the wound healing process. The use of artificial intelligence (AI) as advanced computer-assisted methods is promising for gaining insights into wound assessment and healing. As AI-based approaches have been explored for various applications in wound care and research, this paper provides an overview of recent studies exploring the application of AI and its technical developments and suitability for accurate wound assessment and prediction of wound healing. Several studies have been done across the globe, especially in North America, Europe, Oceania, and Asia. The results of these studies have shown that AI-based approaches are promising for wound assessment and prediction of wound healing. However, there are still some limitations and challenges that need to be addressed. This paper also discusses the challenges and limitations of AI-based approaches for wound assessment and prediction of wound healing. The paper concludes with a discussion of future research directions and recommendations for the use of AI-based approaches for wound assessment and prediction of wound healing

Medical Image Segmentation with Deep Convolutional Neural Networks

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images are time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images has been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and sparked research interests in medical image segmentation using deep learning. We propose three convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published

Medical Image Segmentation Using Machine Learning

Image segmentation is the most crucial step in image processing and analysis. It can divide an image into meaningfully descriptive components or pathological structures. The result of the image division helps analyze images and classify objects. Therefore, getting the most accurate segmented image is essential, especially in medical images. Segmentation methods can be divided into three categories: manual, semiautomatic, and automatic. Manual is the most general and straightforward approach. Manual segmentation is not only time-consuming but also is imprecise. However, automatic image segmentation techniques, such as thresholding and edge detection, are not accurate in the presence of artifacts like noise and texture. This research aims to show how to extract features and use traditional machine learning methods like a random forest to obtain the most accurate regions of interest in CT images. In addition, this study shows how to use a deep learning model to segment the wound area in raw pictures and then analyze the corresponding area in near-infrared images. This thesis first gives a brief review of current approaches to medical image segmentation and deep learning background. Furthermore, we describe different approaches to build a model for segmenting CT-Scan images and Wound Images. For the results, we achieve 97.4% accuracy in CT-image segmentation and 89.8% F1-Score For wound image segmentation

Imparting 3D representations to artificial intelligence for a full assessment of pressure injuries.

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning methods. The main objective of this dissertation is to prove the efficiency of Deep Learning techniques in tackling one of the important health issues we are facing in our society, through medical imaging. Pressure injuries are a dermatology related health issue associated with increased morbidity and health care costs. Managing pressure injuries appropriately is increasingly important for all the professionals in wound care. Using 2D photographs and 3D meshes of these wounds, collected from collaborating hospitals, our mission is to create intelligent systems for a full non-intrusive assessment of these wounds. Five main tasks have been achieved in this study: a literature review of wound imaging methods using machine learning techniques, the classification and segmentation of the tissue types inside the pressure injury, the segmentation of these wounds and the design of an end-to-end system which measures all the necessary quantitative information from 3D meshes for an efficient assessment of PIs, and the integration of the assessment imaging techniques in a web-based application

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients

SEGMENTASI CITRA PADA LUKA KRONIS MENGGUNAKAN METODE FUZZY C-MEANS

Pada umumnya dibutuhkan waktu penyembuhan yang lebih lama untuk penanganan luka kronis, dibutuhkan juga perawatan yang bervariasi untuk menangani luka kronis. Hal ini dikarenakan luka kronis dapat digolongkan sebagai luka yang memiliki tingkat kerumitan cukup rumit untuk dipisahkan, terlebih pada area luka dan area non luka yang memiliki susunan warna yang cenderung meliki kesamaan. Penelitian ini berfokus pada pemisahan area luka dan area non luka menggunakan metode segmentasi algoritma Fuzzy C-means. Percobaan dilakukan dengan proses pre-processing pada citra luka pressure ulcers menggunakan 2 metode, yaitu metode filtersisasi homomorphic dan metode thresholding yang kemudian citra luka pressure ulcers diproses menggunakan algoritma Fuzzy C-means. Hasil dari percobaan segmentasi luka kronis ini menunjukkan bahwa metode Fuzzy C-means dapat dikatakan cukup efektif untuk digunakan dan dapat memisahkan bagian luka dan bagian non luka.Pada umumnya dibutuhkan waktu penyembuhan yang lebih lama untuk penanganan luka kronis, dibutuhkan juga perawatan yang bervariasi untuk menangani luka kronis. Hal ini dikarenakan luka kronis dapat digolongkan sebagai luka yang memiliki tingkat kerumitan cukup rumit untuk dipisahkan, terlebih pada area luka dan area non luka yang memiliki susunan warna yang cenderung meliki kesamaan. Penelitian ini berfokus pada pemisahan area luka dan area non luka menggunakan metode segmentasi algoritma Fuzzy C-means. Percobaan dilakukan dengan proses pre-processing pada citra luka pressure ulcers menggunakan 2 metode, yaitu metode filtersisasi homomorphic dan metode thresholding yang kemudian citra luka pressure ulcers diproses menggunakan algoritma Fuzzy C-means. Hasil dari percobaan segmentasi luka kronis ini menunjukkan bahwa metode Fuzzy C-means dapat dikatakan cukup efektif untuk digunakan dan dapat memisahkan bagian luka dan bagian non luka