How Image Annotation is empowering Medical AI

The value of machine learning in healthcare is its ability to process huge data sets beyond the scope of human capability, and then reliably convert analysis of that data into clinical insights that aid physicians in planning and providing care, ultimately leading to better outcomes, lower costs of care, and increased patient satisfaction.

If your ultimate goal is to train machine learning models, there are few differences between annotating a medical image versus a regular PNG or JPEG. Radiologists annotate or markup medical images on a daily basis. This can be done in DICOM viewers, which contain basic annotation capabilities such as bounding boxes, arrows, and sometimes polygons. Machine learning may sometimes leverage these labels, however, their format is often inconsistent with the needs of ML research, such as lack of instance IDs, attributes, a labeling queue, or the correct formats for deep learning frameworks like Pytorch or TensorFlow.

Medical Image Annotation

Medical image annotation is the bread and butter of all machine learning development in the healthcare sector. Precisely annotated images are required to train models with accuracy, and an enormous amount of such data is needed for AI solutions to make assessments and predictions with confidence. Without the dedicated work of the thousands of human beings laboring to perform the time-consuming, repetitive tasks of data annotation, it would be impossible to develop advanced algorithms that may someday be able to diagnose and predict a wide-ranging variety of pathologies.

In the medical imaging field, annotation is used to draw attention (sometimes using boxes, circles, or arrows) to regions of interest. In the related digital imaging field, the term annotation describes adding metadata to an image in order to train a computer model to recognize certain features. Typically, a medical image annotator performs one of two types of annotation. The first kind, segmentation, involves classifying single pixels. The second kind is classifying a whole image within a dataset. Images are manipulated and encoded in the standard Digital Imaging and Communications in Medicine (DICOM) format. Another widely used format is NIfTI, which produces a 3D image (as opposed to the single slices format of DICOM). Depending on the reader, this format can be manipulated as well.

With the ever-increasing amount of patient data generated in hospitals and the need to support a patient diagnosed with this data, computerized automatic and semiautomatic algorithms are a promising option in the clinical field. An initial step in the development of such systems for diagnosis aid is to have manually annotated datasets that are used to train and implement machine-learning methods to mimic a human annotator. The manual segmentation of the patients’ 3D volumes is commonly used for radiology imaging in order to separate various structures in the images and allow processing tissue of the structures separately. Manual segmentation, on the other hand, demands intensive and time-consuming labor from radiologists.

Types of Documents Annotated through Medical Image Annotation:

There is no shortage of areas where computer vision could bring groundbreaking innovation to medical imaging: CT, MRI, ultrasound, X-rays, and more are just a few of the use cases.

X-Rays

The role of X-rays is to identify if there are any abnormalities or damage to a human organ or body part. Computer vision can be trained to classify scan results just like a radiologist would do and pinpoint all potential problems in a single take.

MRI

Problems in softer tissues, like joints and the circulatory system, are better highlighted by magnetic resonance imaging (MRI). Training a computer vision system to identify clogged blood vessels and cerebral aneurysms can help save those patients who would be under the radar if the images were analyzed by the naked eye.

Ultrasound

Using computer vision during pregnancy and for other routine check-ups could help future mothers see if the pregnancy is unfolding naturally or there are any health concerns to take into consideration. Relying on extensive data sets that combine years of medical knowledge, computer vision-equipped ultrasound systems can show more experience than a single physician would.

CT scans

The advantage of using computer vision here is that the entire process can be automated with increased precision since the machine could identify even those details that are invisible to the human eye. This method is used to detect tumors, internal bleeding, and other life-threatening conditions

Final Thoughts

As more data is available, we have better information to provide patients. Predictive algorithms and machine learning can give us a better predictive model of mortality that doctors can use to educate patients. As larger datasets begin to run machine learning, we can improve care in more specific ways for each region. And considering rare diseases with low data volumes, it should be possible to merge regional data into national sets to scale the volume needed for machine learning.

To annotate the medical image dataset for AI in healthcare, TagX provides a highly accurate medical image annotation service. It has the ability to accurately annotate a large number of radiological images.