CT Image Segmentation for Subdural Hematoma Drainage Site Optimization
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Solution Overview
Problem
Current methods for assessing brain atrophy and determining optimal drainage sites for chronic subdural hematomas in CT imaging are limited by lower contrast/noise and radiation exposure, and suboptimal placement of drainage sites leads to recurrence and poor outcomes in patients.
Innovation Solution
A system and method for determining brain atrophy and optimal drainage sites using CT scans, involving image segmentation, morphological erosion, and density analysis to identify the centroid and shape of subdural hematomas, guiding the placement of twist drill craniostomy for improved drainage and reduced recurrence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CT imaging is used to assess brain atrophy, then cost is reduced and acquisition speed is increased, but contrast resolution and noise performance deteriorate
Solution Approach 1:
The patent applies preliminary action by performing morphological erosion and opening operations on the CT image data before atrophy assessment. These preprocessing steps enhance the contrast and reduce noise in the brain tissue regions, compensating for the inherently lower contrast resolution of CT compared to MRI. The erosion operation removes unwanted structures and enhances the visibility of brain atrophy patterns.
2Object-affected harmful factors
If CT imaging is used to assess brain atrophy, then radiation exposure is avoided compared to MRI, but image quality and soft tissue contrast deteriorate
Solution Approach 1:
The patent applies parameter changes by transforming the CT image data through morphological operations that alter the spatial and intensity parameters of the image. The erosion and opening operations change the parameter distribution to enhance soft tissue contrast visualization. Additionally, the system calculates atrophy metrics by comparing parameter changes across serial scans, effectively compensating for the lower inherent contrast of CT imaging.
3Ease of operation
If drainage site placement is based on visual estimation, then procedural simplicity is maintained, but drainage efficacy deteriorates leading to recurrence
Solution Approach 1:
The patent replaces the mechanical/visual estimation method with an automated computational system. The system uses image processing algorithms to automatically identify the hematoma centroid, calculate optimal drainage site coordinates, and determine the required drill depth. This substitution maintains ease of operation while dramatically improving drainage efficacy through precise, objective measurements rather than subjective visual estimation.
4Loss of time
If twist drill craniostomy is performed with approximate drainage site placement, then procedural time is reduced, but treatment outcomes deteriorate with higher recurrence rates
Solution Approach 1:
The system performs preliminary computational analysis to determine the optimal drainage site and drill depth before the actual procedure. By pre-calculating the precise parameters needed for effective drainage, the system eliminates the need for time-consuming intraoperative adjustments or trial-and-error approaches. The preliminary image processing and coordinate calculation are completed automatically, providing the surgeon with exact placement guidelines that improve outcomes without extending procedural time.
Data Source
AI summary
To that end, in order to overcome some of the deficiencies presented herein above, an exemplary system, method and computer-accessible medium for determining an attribute(s) of a brain of a patient, can include, for example, receiving information obtained from a computed tomography (“CT”) scan(s) of a portion(s) of the brain, generating a CT image(s) that can be based on the information, and determining the attribute(s) of the brain based on the CT image(s) by segmenting an intracranial space (ICS) in the CT image(s). The attribute(s) can include a presence or absence of Alzheimer's disease, total volume of the ICS, brain, CSF or a lesion or the volumes of ICS, brain, CSF or lesion(s) expressed as a percentage of other volume(s). The aforementioned areas can be segmented using a combination of thresholding, morphological erosions, morphological dilations, manual segmentation or semi-automatic segmentation techniques, all of which can be parallel procedures. These attributes can be further used to determine treatment, for example, optimizing the location of the twist drill craniotomy to drain hematoma in subdural hematoma.