POC Brain Tumor Segmentation. vlife Use Case

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1 Brain Tumor Segmentation vlife Use Case 1

2 Automatic Brain Tumor Segmentation using CNN Background Brain tumor segmentation seeks to separate healthy tissue from tumorous regions such as the advancing tumor, necrotic core and surrounding edema. This is an essential step in diagnosis and treatment planning, both of which need to take place quickly in the case of a malignancy in order to maximize the likelihood of successful treatment. Due to the slow and tedious nature of manual segmentation, there is a high demand for computer algorithms that can do this quickly and accurately. Approach In this proof of concept we used CNN (Convolutional Neural Networks) in MRI Images to do brain tumor segmentation. The algorithm uses CNNs for semantic segmentation of images and can distinguish between tumor and healthy tissue, actively enhancing tumor and non-advancing tumor regions. With automated brain tumor segmentation the enhanced image output can now assist the clinicians to focus more on patient care and improve the chances of survival in limited time by potentially decreasing the lag time between diagnostic tests and starting the care plan 2

3 Automatic Brain Tumor Segmentation Using CNN Dataset For the POC we leveraged the BraTS 2015 dataset The dataset consists of 300 patient cases classified into 250 high grade glioma, 50 low grade glioma cases Each patient s case has 5 MRI scans T1, T1c, T2, Flair, Ground Truth Each MRI scan has 155 brain slices attached to it Brain MRI scans are in.mha format Magnetic Resonance Imaging (MRI) is the most common diagnostic tool for brain tumors primarily due to its noninvasive nature and ability to image diverse tissue types and physiological processes. MRI uses a magnetic gradient and radio frequency pulses to take repetitive axial slices of the brain and construct a 3-dimensional representation (Figure 2). Each brain scan 155 slices, with each pixel representing a 1mm3 voxel Challenges: One of the challenges in working with MRI data is dealing with the artifacts produced either by inhomogeneity in the magnetic field or small movements made by the patient during scan time. This can effect a segmentation result particularly in the setting of computer-based models. To overcome this N4ITK bias correction was used which removed the intensity gradient on each scan. Also, additional image pre-processing requires standardizing the pixel intensities, since MRI intensities are expressed in arbitrary units and may differ significantly between machines used and scan times. 3

4 Solution Architecture Radiologist /User Input image Single patient has 5 MRI scans - T1, T1c, T2, Flair and Ground Truth CNN model 4 MRI Scans T1, T1c, T2, and Flair will be used as an input to model Output image Physician Ground Truth can be used by model to verify prediction result Patient Model Architecture The model can distinguish and predict between healthy tissue, actively enhancing tumor and non-advancing tumor regions The local invariant nature of CNNs allows for abstraction of token features for classification without relying on large-scale spatial information that is inconsistent in the case of tumor location Small patches of images were generated to train the CNN Three combinations of intermediate layer Convolutional, ReLU and pooling layer were used to train CNN model Final prediction was made using ANNs as the last layer 4

5 Output and Opportunities Output The model can accurately identify each of the four classes Potential Opportunities A single patient will produce upwards of 600 images from a single MRI, given that all four sequences produce 155 slices each To get a satisfactory manual segmentation a radiologist must spend several hours tediously determining which voxels belong to which class. In this approach using CNN would give clinicians more time to focus on the well being of the patient allowing for more immediate patient care and higher throughput treatment times. 5

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