Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

Abstract

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. Dataset: https://physionet.org/content/ct-ich/1.2.0/,

Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks

“…After the initial success of deep learning [ 10 ] in object recognition from images [ 3 , 11 ], deep neural networks have been adopted for a broad range of tasks in medical imaging, ranging from cell segmentation [ 12 ] and cancer detection [ 13 , 14 , 15 , 16 , 17 ] to intracranial hemorrhage detection [ 5 , 8 , 18 , 19 , 20 , 21 , 22 ] and CT/MRI super-resolution [ 23 , 24 , 25 , 26 ]. Since we address the task of intracranial hemorrhage detection, we consider related works that are focused on the same task as ours [ 5 , 6 , 7 , 8 , 18 , 19 , 20 , 21 , 22 , 27 , 28 , 29 , 30 ], as well as works that study intracranial hemorrhage segmentation [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ].…”

“…While most of the recent works proposed deep learning approaches such as convolutional neural networks [ 18 , 20 , 21 , 22 , 27 , 29 , 30 , 37 ], fully-convolutional networks (FCNs) [ 19 , 32 , 33 , 36 , 38 , 39 ] and hybrid convolutional and recurrent models [ 5 , 6 , 7 , 8 ], there are still some recent works based on conventional machine learning methods, e.g., superpixels [ 43 , 44 ], fuzzy C-means [ 31 , 35 ], level set [ 42 , 43 ], histogram analysis [ 41 ], thresholding [ 40 ] and continuous max-flow [ 34 ].…”

“…For intracranial hemorrhage segmentation, there are a few works [ 32 , 36 , 38 , 39 ] that employed the U-Net model [ 12 ], a fully-convolutional network shaped like an auto-encoder with skip connections. Hssayeni et al [ 36 ] used the standard U-Net model, their main contribution being a dataset of 82 CT scans. Cho et al [ 32 ] combined the conventional U-Net architecture with an affinity graph to learn pixel connectivity and obtain smooth (less noisy) segmentation results.…”

“…The authors reported accuracy rates comparable to that of trained radiologists for acute intracranial hemorrhage detection. The state-of-the-art methods based on FCN models [ 19 , 32 , 33 , 36 , 38 , 39 ] are mainly focused on the segmentation task, whereas our focus is on the detection task. Some of the FCN models are based on 3D convolutions, obtaining better segmentations by considering adjacent CT slices.…”

A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT

“…Given the clinical significance, numerous attempts at developing computer-assisted automation tools for hematoma volume have been reported [13][14][15][16] . However, many of the techniques even till recent are either manual, semi-automatic, slow, or suffer from poor accuracy, especially for irregular hematoma shapes and sizes [13][14][15][16][17][18][19] . Great efforts have been geared towards building automated segmentation tools with use of artificial intelligence technologies, in particular the promising deep-learning algorithms 20,21 .…”