Role of Computer Vision, Machine Learning in Medical Imaging
Computer vision techniques are advancing rapidly this has triggered its extensive use in the area of medical imaging by Healthcare Professionals. The goal of adapting computer vision techniques in medical imaging is to make disease detection fast and accurate with intelligent algorithms that enable better medical decisions.
Computer vision aids in better diagnosis, treatment, and medication of disease by extracting useful information like texture, shape, and contour information of the image along with contextual information. The information is then presented in the form of 3D and 4D modeling for better understanding.
Indeed, medical imagery is a vast field that requires the potential for developing novel and effective computer vision algorithms. The rise of computer vision techniques in this field has changed the conventional medical imagery in all aspects but still, the use of computer vision is a challenge for clinicians due to its complexity.
How does Computer Vision Powered Medical Imaging Work?
One of the key features of medical imagery is the generation of huge voluminous data that grows exponentially due to the high quality of data capturing tools. The data is associated with the patient’s condition, development of the disease, and the suitable treatment for the cure. \
A series of patients’ medical images shows the big picture of the patient’s health which is difficult to put into one place. However, doctors can’t consider these images due to workload, lack of time, and expertise. To solve this problem data interpretation is necessary which is possible by adapting the computer vision techniques to process the data and get valuable insights with high speed and accuracy.
The primary operations handled by the deep learning models in medical imagery are the image classification that involves the preprocessing step and identification of lesions based on the feature descriptors, object localization which involves the process of 3D parsing i-e converting a 3D image into a 2D image, object segmentation which is used for the identification of the objects for quantitative analysis, spatial alignment used for the transformation of the coordinates from one sample to another, image generation and enhancement involves image normalization used for improving the quality of an image to detect the anomalies and at last, content-based image retrieval which is used for extracting knowledge from a large dataset.
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Different computer vision techniques (like deep learning and neural networks) are used to extract useful features from medical imagery. In this blog, let’s explore the use cases of computer vision applications in medical imagery.
Computer Vision Use cases in Medical Imagery
- Cardiac Assessment
Cardiac imagery refers to diagnostic radiology and is performed by physicians to evaluate the anatomy and diagnose the heart diseases like leaky heart valves, changes in the size and shape of the heart, poor functioning of the heart chambers, and pericardium.
Radiologists use cardiac imagery obtained using X-rays, echocardiograms, computed tomography, and MRI scan to determine the severity of the heart disease. The choice of cardiac test depends upon the nature and complexity of the heart disease being investigated.
Cardiac assessment requires a lot of data to spot the symptoms by monitoring the heart and find a suitable cure quickly. Cardiovascular pathologies need quick action to handle heart disease which can be handled by deep learning models.
Deep learning-based cardiac imagery is used to find the blood flow quantization to measure its rate and estimating the blood flow features, finding the anomalies using the quantitative data, and show a data visualization of the outcome.
Deep learning allows the physician to have a quick insight into the data to take suitable precautionary measures in a short period. For example, to measure the change in the heart shape the cardiac imagery identifies a set of pixels that illustrates the contour of the heart.
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- Cancer Detection
The rise of computer vision techniques has a great impact on pathology. Cancer detection using deep learning techniques has been widely used all over the world and shows promising results. Deep learning not only gives precise and accurate outcome but also speed up the process.
Cancer like Melanoma and breast cancer if diagnosed at an early stage have high chances of curability. Similarly, deep learning-based medical imagery aids in tracking and analyzing the development of metastatic cancer. For instance, the Lymph Node Assistant (LYNA) model is trained on pathological slides datasets. The model can quickly identify tumors and meta states in a short span with 99% accuracy.
Similarly, deep learning is also used to detect skin cancer to identify anomalies and track its development whereas in breast cancer detection neural network is used to identify anomalous tissues by comparing the mammogram images.
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- Tumor Detection
Computer vision plays a big role in tumor detection by processing a large number of images and extracting valuable insights. Tumors are the most aggressive disease that increases the death risk if not cured at an early stage.
Thus, early treatment planning is a key step to save the patient’s life. Tumor development tracking in various organs like breast, brain, lungs, liver, and prostate needs various image techniques like MRI, ultrasound, and CT scan. The images obtained with the help of these techniques revealed critical information like tumor location, size, shape, and density.
In addition, Convolutional Neural Network (CNN) is used for image pre-processing, feature extraction, and classification to detect the tumor and its possible characteristics. The machine learning-based analytical models are used to analyze tumor proliferation. The widely used model is the tumor probability heatmap aids in the classification of tumors based on tissue map overlap.
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- Retinal Blood Vessel Segmentation
Segmenting the blood vessels is one crucial task in medical imaging due to the low pixels contrast and complex feature information. The blood vessel structure analysis from the fundus images is important as many systematic and ophthalmologic diseases cause observable pathological modification such as eye disease, hypertension, and diabetic retinopathy.
Several feature descriptors of retinal veins and arteries (like diameter, length, angle, tortuosity) have been used for disease diagnostic and to monitor the disease progression and treatment.
Manual segmentation is a repetitive task that is time-consuming and requires training and skills. For that automatic segmentation is necessary which is possible through deep learning models. Such networks are trained using the Structured Analysis of the Retina (STARE) dataset containing 28999×960 labeled images. Deep learning models enhance the image quality and substructure segmentation is used for the accurate segmentation of blood vessels.
In short, the implementation of computer vision techniques reduces the overall time of the workflow. On the other hand, trained deep learning models have the capability of labeling the images and key points of the image for a better segmentation process. Physicians are shifting to the intelligent models of segmentation rather than using the traditional way of annotating and segmenting images which takes a lot of time and effort.
- Osteoporosis Screening
Potential convolution neural network has a significant role in osteoporosis screening for extracting the useful information of bones fractures and bone diseases such as Spur, Rickets, Bone cancer, Osteonecrosis, and so forth. Bones injury and diseases for the long term can cause severe pain and disability.
To solve this problem, medical imaging is used to examine the structure of the bone. One of the most common methods of bone screening is X-Ray. Although most of the
bones can be visually identified in CT images without difficulties, a precise automated segmentation is still very challenging. CT scan images provide low-resolution images which can be dealt with by Convolutional Neural Network. CNN enhances the image quality, classifies the image based on the feature descriptors (like bone type), and segments the bone anomalies like (bone break, fractures, and spur). Thus, use of CNN in bone imagery can make the radiologist job more accessible and effective.
Data Annotation in Medical Imagery
The medical images obtained through MRI, CT scan, or ultrasound are annotated based on diverse annotation techniques like polygon mesh, bounding box, and so forth. Medical image labeling plays a significant role in the detection of disease and medical ailments with high accuracy using efficient deep learning models.
The labeled images are passed for the training of the deep learning models to get the desired outcome. The curated data annotation techniques help the companies in the development of cutting-edge AI-based healthcare applications.
For instance, brain tumors are detected using deep learning models that are fully trained on annotated datasets (like brain injuries and tumor annotation). These models are used by radiologists to detect brain anomalies. Similarly, ML or DL models trained on cancerous datasets are used to predict symptoms of cancer. The trained models help the radiologist to cure this life-threatening disease if diagnosed at an early stage.
One of the most reliable platforms for the annotation of medical imagery is Labellerr. It provides high-quality training data for the development of deep learning models. It facilitates in providing annotations of all format datasets including DICOM and NIFTI to ensure the processing and originality of the data.
Medical imagery produces voluminous data which needs to be interpreted. To make the radiologist’s job fast and easier, the implementation of computer vision techniques is a smart choice.
Computer vision techniques have the capability of extracting useful insights from the medical imagery that is helpful in the radiologist job. Not only this, these techniques provide quick access to data with high precision and accuracy, and trained neural networks aids in the refinement of the images for further processing.