MIA: Medical Image Annotation via non-expert Crowdsourcing
In this project we harness the wisdom of non-experts via crowdsourcing to contour clinical images, specifically of lung cancer to precisely identify tumors, in order to be fed into a machine learning algorithm to help precise and automated detection of tumors.
Lung cancer is the most deadly cancer in the world, claiming over 2.5 million lives yearly. For cases in which surgery is not an option, chemoradiotherapy is the standard treatment modality. However, numerous other treatment options exist, such as immunotherapy and a variety of systemic anti-cancer therapies. In order to personalize treatment, we extract quantitative imaging features from the tumor of the patient.
These quantitative image features can provide information as to which treatment would be most effective for this patient, based on patient treated in the past. In order to extract these quantitative image features, however, we need to first contour the tumor on the image. This is the most time-intensive step of the entire process of treatment personalization.
Involving experts i.e. doctors to annotate images with precise contouring is the current gold standard, however this cannot scale and with the ever-increasing amounts of clinical image data can become expensive and time consuming. Therefore, we require a scalable, affordable and quicker means of annotating large amounts of tumor images precisely.
Thus, in this project we harness the wisdom of non-experts via crowdsourcing to contour clinical images, specifically of lung cancer to precisely identify tumors. If we can do this task properly of contouring tumors in images, we will be able to identify the best treatment (in terms of survival and quality of life) for the patients. This will enable patients to be properly informed about each treatment option and has the potential to save lives and increase quality of life for cancer patients.