Intracranial Hemorrhage Segmentation Using Deep Convolutional Model
This addresses the need for automated ICH segmentation to assist radiologists in time-sensitive diagnosis, but it is incremental as it applies an existing method (U-Net) to a new medical dataset.
The paper tackled the problem of segmenting intracranial hemorrhage (ICH) from CT scans using a deep fully convolutional network (U-Net), achieving a Dice coefficient of 0.31 on a dataset of 82 CT scans.
Traumatic brain injuries could cause intracranial hemorrhage (ICH). ICH could lead to disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure. The current clinical protocol to diagnose ICH is examining Computerized Tomography (CT) scans by radiologists to detect ICH and localize its regions. However, this process relies heavily on the availability of an experienced radiologist. In this paper, we designed a study protocol to collect a dataset of 82 CT scans of subjects with traumatic brain injury. Later, the ICH regions were manually delineated in each slice by a consensus decision of two radiologists. Recently, fully convolutional networks (FCN) have shown to be successful in medical image segmentation. We developed a deep FCN, called U-Net, to segment the ICH regions from the CT scans in a fully automated manner. The method achieved a Dice coefficient of 0.31 for the ICH segmentation based on 5-fold cross-validation. The dataset is publicly available online at PhysioNet repository for future analysis and comparison.