A Graphical Approach For Brain Haemorrhage Segmentation
This addresses a critical medical problem for diagnosing neurological emergencies like stroke and traumatic brain injury, but it appears incremental as it builds on existing deep learning methods.
The paper tackles brain hemorrhage segmentation from CT scans by proposing a hybrid architecture combining CNNs and GNNs, achieving a dice coefficient score of around 0.81 with limited data.
Haemorrhaging of the brain is the leading cause of death in people between the ages of 15 and 24 and the third leading cause of death in people older than that. Computed tomography (CT) is an imaging modality used to diagnose neurological emergencies, including stroke and traumatic brain injury. Recent advances in Deep Learning and Image Processing have utilised different modalities like CT scans to help automate the detection and segmentation of brain haemorrhage occurrences. In this paper, we propose a novel implementation of an architecture consisting of traditional Convolutional Neural Networks(CNN) along with Graph Neural Networks(GNN) to produce a holistic model for the task of brain haemorrhage segmentation.GNNs work on the principle of neighbourhood aggregation thus providing a reliable estimate of global structures present in images. GNNs work with few layers thus in turn requiring fewer parameters to work with. We were able to achieve a dice coefficient score of around 0.81 with limited data with our implementation.