Chun-Chih Liao

Cranioplasty for patients with severe depressed skull bone defect after cerebrospinal fluid shunting.

Cranioplasty is indicated for patients with a skull bone defect. Patients may achieve subjective and objective improvements after cranioplasty. Some patients with severe brain swelling treated with decompressive craniectomy may develop hydrocephalus associated with severe brain bulging or even herniation via the skull bone defect. Consequently, these patients require a ventriculoperitoneal (V-P) shunt to relieve hydrocephalus. However, after shunting for hydrocephalus, they may develop severe sinking at the skull defect. Subsequently, when doing a cranioplasty for such a depressed defect, it may result in the dysfunction of the underlying brain, or even hematoma formation due to the large dead space. In this study, we advocate a temporary procedure to occlude the V-P shunt tube to allow the expansion of a depressed scalp flap to facilitate the subsequent cranioplasty. We report four patients with severe depression of the skull defect resulting from previous traumatic brain swelling followed by decompressive craniectomy and V-P shunting for communicating hydrocephalus. A simple subcutaneous clipping of the shunt tube was performed to allow the expansion of the depressed scalp to obliterate the dead space before the cranioplasty. All four patients obtained a satisfactory result without complications and achieved good functional recovery. A temporary occlusion of the shunt tube with an aneurysm clip before cranioplasty for patients with a severely depressed scalp flap is a simple and useful procedure. This procedure can safely and effectively eliminate the dead space between the skull plate and the dura to facilitate the cranioplasty, and thus prevent the potential complication of intracranial hematoma.

Automatic recognition of midline shift on brain CT images

Midline shift is one of the most important quantitative features clinicians use to evaluate the severity of brain compression by various pathologies. It can be recognized by modeling brain deformation according to the estimated biomechanical properties of the brain and the cerebrospinal fluid spaces. This paper proposes a novel method to identify the deformed midline according to the above hypothesis. In this model, the deformed midline is decomposed into three segments: the upper and the lower straight segments representing parts of the tough dura mater separating two brain hemispheres, and the central curved segment formed by a quadratic Bezier curve, representing the intervening soft brain tissue. The deformed midline is obtained by minimizing the summed square of the differences across all midline pixels, to simulate maximal bilateral symmetry. A genetic algorithm is applied to derive the optimal values of the control points of the Bezier curve. Our algorithm was evaluated on pathological images from 81 consecutive patients treated in a single institute over a period of one year. Our algorithm is able to recognize the deformed midlines in 65 (80%) of the patients with an accuracy of 95%, making it a useful tool for clinical decision-making.

A simple genetic algorithm for tracing the deformed midline on a single slice of brain CT using quadratic Bezier curves

Midline shift (MLS) is one of the most important quantitative features clinicians use to evaluate the severity of brain compression. It can be recognized by modeling brain deformation according to the estimated biomechanical properties of the brain structures. This paper proposes a novel method to identify the deformed midline by decomposing it into three segments: the upper and the lower straight segments representing parts of the tough meninges separating two brain hemispheres, and the central curved segment formed by a quadratic Bezier curve, representing the intervening soft brain tissue. The deformed midline is obtained by minimizing the summed square of the differences across all midline points, applying a genetic algorithm. Our algorithm was evaluated on images containing various pathologies from 81 consecutive patients treated in a single institute over one-year period. The deformed midlines were evaluated by human experts, and the values of midline shift were accurate in 95%

TRACING THE DEFORMED MIDLINE ON BRAIN CT

Midline shift (MLS) is the most important quantitative feature clinicians use to evaluate the severity of brain compression by various pathologies. We proposed a model of the deformed midline according to the biomechanical properties of different types of intracranial tissue. The model comprised three segments. The upper and lower straight segments represented parts of the tough meninges separating two hemispheres, and the central curved segment, formed by a quadratic Bezier curve, represented the intervening soft brain tissue. For each point of the model, the intensity difference was calculated over 48 adjacent point pairs at each side. The deformed midline was considered ideal as summed square of the difference across all midline points approaches global minimum, simulating maximal bilateral symmetry. Genetic algorithm was applied to optimize the values of the three control points of the Bezier curve. Our system was tested on images containing various pathologies from 81 consecutive patients treated in a single institute over one-year period. The deformed midlines itself as well as the amount of midline shift were evaluated by human experts, with satisfactory results.

Computer-aided diagnosis of intracranial hematoma with brain deformation on computed tomography

Physicians evaluate computed tomography (CT) of the brain to quantitatively and qualitatively identify various types of intracranial hematomas for patients with neurological emergencies. We propose a novel method that can perform this task in a totally automatic fashion, based on a multiresolution binary level set method. The skull regions are segmented in downsized images generated with a maximum filter. The intracranial regions are located using the average gray levels and connectivity. These regions compose the regions of interest (ROIs) for segmenting the hematoma from the normal brain. The gray levels of the voxels within these ROIs are generated with an averaging filter in a multiresolution fashion. After identifying the candidate hematoma voxels using adaptive thresholds and connectivity, binary level set algorithm is applied repeatedly until the original resolution is reached. We apply our method to non-volumetric non-contrast CT images of 15 surgically proven intracranial hematomas and the results were quantitatively evaluated by a human expert. The correlation coefficient between the volumes measured manually and automatically is 0.97. The overlap metrics ranged from 0.97 to 0.74, with an average of 0.88. The average precision and recall are 0.89 and 0.87, respectively. We use decision rules to classify these hematomas and were able to make correct diagnoses in all cases.

Automatic MRI Meningioma Segmentation Using Estimation Maximization

With the advancement of the imaging facility and image processing technique, computer assisted surgical planning and image guided technology have become increasingly used in neurosurgery. For MRI has the characteristic of multi-spectral image data, so knowledge-base techniques is widely used in brain MRI segmentation. Here we recognize the location of the tumor automatically and provide an accurate result by estimation maximization method. Simultaneously, promote the efficiency of reading image as well

Automated assessment of midline shift in head injury patients.

OBJECTIVES Midline shift (MLS) is an important quantitative feature for evaluating severity of brain compression by various pathologies, including traumatic intracranial hematomas. In this study, we sought to determine the accuracy and the prognostic value of our computer algorithm that automatically measures the MLS of the brain on computed tomography (CT) images in patients with head injury. PATIENTS AND METHODS Modelling the deformed midline into three segments, we had designed an algorithm to estimate the MLS automatically. We retrospectively applied our algorithm to the initial CT images of 53 patients with head injury to determine the automated MLS (aMLS) and validated it against that measured by human (hMLS). Both measurements were separately used to predict the neurological outcome of the patients. RESULTS The hMLS ranged from 0 to 30 mm. It was greater than 5 mm in images of 17 patients (32%). In 49 images (92%), the difference between hMLS and aMLS was <1 mm. To detect MLS >5 mm, our algorithm achieved sensitivity of 94% and specificity of 100%. For mortality prediction, aMLS was no worse than hMLS. CONCLUSION In summary, automated MLS was accurate and predicted outcome as well as that measured manually. This approach might be useful in constructing a fully automated computer-assisted diagnosis system.

A knowledge discovery approach to diagnosing intracranial hematomas on brain CT: recognition, measurement and classification

Computed tomography (CT) of the brain is preferred study on neurological emergencies. Physicians use CT to diagnose various types of intracranial hematomas, including epidural, subdural and intracerebral hematomas according to their locations and shapes. We propose a novel method that can automatically diagnose intracranial hematomas by combining machine vision and knowledge discovery techniques. The skull on the CT slice is located and the depth of each intracranial pixel is labeled. After normalization of the pixel intensities by their depth, the hyperdense area of intracranial hematoma is segmented with multi-resolution thresholding and region-growing. We then apply C4.5 algorithm to construct a decision tree using the features of the segmented hematoma and the diagnoses made by physicians. The algorithm was evaluated on 48 pathological images treated in a single institute. The two discovered rules closely resemble those used by human experts, and are able to make correct diagnoses in all cases.

A multiresolution binary level set method and its application to intracranial hematoma segmentation

We propose a multiresolution binary level set method for image segmentation. The binary level set formulation is based on the Song-Chan algorithm, which cannot compute the edge length when the margin of the image is irregular. We modify the edge length approximation so that it can work everywhere in a single-connected image, make it suitable to segment objects at any position, especially near the margin of the image. For multiresolution processing, we use image pyramids. The binary level set method works on images with reduced resolution and size. A point at the image with lower resolution is processed instead of a block or a strip at the original resolution, therefore improving the efficiency. Our multiresolution binary level set method is applied to segmentation of intracranial hematomas on brain CT slices. Segmentation of epidural and subdural hematomas, which have been not done previously, is performed successfully in seconds with results comparable to human experts.

PICO element detection in medical text without metadata

Efficient identification of patient, intervention, comparison, and outcome (PICO) components in medical articles is helpful in evidence-based medicine. The purpose of this study is to clarify whether first sentences of these components are good enough to train naive Bayes classifiers for sentence-level PICO element detection. We extracted 19,854 structured abstracts of randomized controlled trials with any P/I/O label from PubMed for naive Bayes classifiers training. Performances of classifiers trained by first sentences of each section (CF) and those trained by all sentences (CA) were compared using all sentences by ten-fold cross-validation. The results measured by recall, precision, and F-measures show that there are no significant differences in performance between CF and CA for detection of O-element (F-measure=0.731±0.009 vs. 0.738±0.010, p=0.123). However, CA perform better for I-elements, in terms of recall (0.752±0.012 vs. 0.620±0.007, p<0.001) and F-measures (0.728±0.006 vs. 0.662±0.007, p<0.001). For P-elements, CF have higher precision (0.714±0.009 vs. 0.665±0.010, p<0.001), but lower recall (0.766±0.013 vs. 0.811±0.012, p<0.001). CF are not always better than CA in sentence-level PICO element detection. Their performance varies in detecting different elements.