Machine Learning in Radiation Oncology

Automated Brain Tumors Segmentation and Patient Overall Survival Predictions with Machine Learning 

Tumor segmentation (delineating a line around the tumor, or contouring) is conventionally used to be done manually by the radiation oncologist which requite considerable effort and time consuming. Auto-process of this task is significantly reducing the 

challenges in developing an automated tumor delineation tool. 

associated with quality and safety of the patient treatment plan, 

Accurately drawing the tumor in the treatment plan on patient CT is one of the most big challenge in patient treatment chain in radiation oncology. Up to date, there is no software that is quite good enough for this task. In this work, we aimed to develop an automated tool for brain segmentation on a multi modal MRI scans based on state-of-the-art machine learning algorithms and predicts the patient overall survival.   

Fig. 1.Top, Predicted segmentation labels (green), edema tumor, on a T2-Flair MRI BraTs'2017 validation data (slice# 84:2:98) with our automated model. Bottom, A tumor on MRI (left) and its predicted segmentation (right) with coronal, sagital, and axial view. 

Fig. 2. The plot of the OS classification predicted regions (Left) and the confusion matrix (Right). On the confusion matrix, the rows correspond to the predicted class (Output Class), and the columns show the true class (Target Class). The diagonal cells show for how many (and what percentage) of the examples the trained algorithm correctly estimates the classes of observations. That is, it shows what percentage of the true and predicted classes match. The off diagonal cells show where the classifier has made mistakes. The column on the far right of the plot shows the accuracy for each predicted class, while the row at the bottom of the plot shows the accuracy for each true class. The cell in the bottom right of the plot shows the overall accuracy. 

Overview about machine learning

What is Machine learning?

Machine Learning (ML) is actually a set of rules that a computer develops on its own to correctly solve problems. The basic idea is that a ML computer will find patterns in data and then predict the outcome of something it has never seen before. ML is a critical component to any Artificial Intelligence (AI) development.

Machine learning is classified into three categorizes: Supervised Learning, Reinforcement Learning (semi-supervised), and Deep Learning (unsupervised). 

a) Supervised Learning which is a type of machine learning that feeds a computer system many (thousands, millions or even billions) of examples of a given item and having the computer calculate the similarities between those items so that it can recognize other examples of that item which it has not seen yet. 

b) Reinforcement Learning which is a type of machine learning that tells a computer if it has made the correct decision or the wrong decision. With enough iterations a reinforcement learning system will eventually be able to predict the correct outcomes and therefore make the ‘right decision’.

c) Deep Learning is a type of machine learning that requires computer systems to iteratively perform calculations to determine patterns by itself. 

Why ML pop-up recently?
Until recently ML was not possible because we lacked the very large data sets computers require to find patterns in, the storage capacity to keep all of that data and the computing power to find those patterns in a reasonable amount of time. All three of these factors have now changed: 

1. With the advent of parallel processors, usually GPU’s (Graphics Processing Unit’s) the computing power issue has been advanced to the point where Machine Learning is now possible.
2. ‘Big Data’ has now collected enough data samples for Machine Learning to work with.