Segmentation and Fracture Detection in CT Images for Traumatic Pelvic Injuries

In recent decades, more types and quantities of medical data have been collected due to advanced technology. A large number of significant and critical information is contained in these medical data. High efficient and automated computational methods are urgently needed to process and analyze all available medical data in order to provide the physicians with recommendations and predictions on diagnostic decisions and treatment planning. Traumatic pelvic injury is a severe yet common injury in the United States, often caused by motor vehicle accidents or fall. Information contained in the pelvic Computed Tomography (CT) images is very important for assessing the severity and prognosis of traumatic pelvic injuries. Each pelvic CT scan includes a large number of slices. Meanwhile, each slice contains a large quantity of data that may not be thoroughly and accurately analyzed via simple visual inspection with the desired accuracy and speed. Hence, a computer-assisted pelvic trauma decision-making system is needed to assist physicians in making accurate diagnostic decisions and determining treatment planning in a short period of time. Pelvic bone segmentation is a vital step in analyzing pelvic CT images and assisting physicians with diagnostic decisions in traumatic pelvic injuries. In this study, a new hierarchical segmentation algorithm is proposed to automatically extract multiplelevel bone structures using a combination of anatomical knowledge and computational techniques. First, morphological operations, image enhancement, and edge detection are performed for preliminary bone segmentation. The proposed algorithm then uses a template-based best shape matching method that provides an entirely automated segmentation process. This is followed by the proposed Registered Active Shape Model (RASM) algorithm that extracts pelvic bone tissues using more robust training models than the Standard ASM algorithm. In addition, a novel hierarchical initialization process for RASM is proposed in order to address the shortcoming of the Standard ASM, i.e. high sensitivity to initialization. Two suitable measures are defined to evaluate the segmentation results: Mean Distance and Mis-segmented Area to quantify the segmentation accuracy. Successful segmentation results indicate effectiveness and robustness of the proposed algorithm. Comparison of segmentation performance is also conducted using both the proposed method and the Snake method. A cross-validation process is designed to demonstrate the effectiveness of the training models. 3D pelvic bone models are built after pelvic bone structures are segmented from consecutive 2D CT slices. Automatic and accurate detection of the fractures from segmented bones in traumatic pelvic injuries can help physicians detect the severity of injuries in patients. The extraction of fracture features (such as presence and location of fractures) as well as fracture displacement measurement, are vital for assisting physicians in making faster and more accurate decisions. In this project, after bone segmentation, fracture detection is performed using a hierarchical algorithm based on wavelet transformation, adaptive windowing, boundary tracing and masking. Also, a quantitative measure of fracture severity based on pelvic CT scans is defined and explored. The results are promising, demonstrating that the proposed method not only capable of automatically detecting both major and minor fractures, but also has potentials to be used for clinical applications

Comparison of Fracture Delineation Methods in Anteroposterior Pelvic Radiographs

Pelvic fractures are very difficult to detect due to the visual complexity of the pelvic bone. Pelvic fracture occurs less frequently, only when there is a high energy event such as fall from a height or vehicle collision. In elder people and in osteoporosis patients even a low energy incident may cause fracture. The paper includes the comparison of three different fracture detection methods – GLCM and ANN based, Statistical curve fitting and classifier based and finally statistical curve fitting and ANN based method

Hemorrhage Detection and Analysis in Traumatic Pelvic Injuries

Traumatic pelvic injuries associated with high-energy pelvic fractures are life-threatening injuries. Extensive bleeding is relatively common with pelvic fractures. However, bleeding is especially prevalent with high-energy fractures. Hemorrhage remains the major cause of death that occur within the first 24 hours after a traumatic pelvic injury. Emergent-life saving treatment is required for high-energy pelvic fractures associated with hemorrhage. A thorough understanding of potential sources of bleeding within a short period is essential for diagnosis and treatment planning. Computed Tomography (CT) images have been widely in use in identifying the potential sources of bleeding. A pelvic CT scan contains a large number of images. Analyzing each slice in a scan via simple visual inspection is very time consuming. Time is a crucial factor in emergency medicine. Therefore, a computer-assisted pelvic trauma decision-making system is advantageous for assisting physicians in fast and accurate decision making and treatment planning. The proposed project presents an automated system to detect and segment hemorrhage and combines it with the other extracted features from pelvic images and demographic data to provide recommendations to trauma caregivers for diagnosis and treatment. The first part of the project is to develop automated methods to detect arteries by incorporating bone information. This part of the project merges bone edges and segments bone using a seed growing technique. Later the segmented bone information is utilized along with the best template matching to locate arteries and extract gray level information of the located arteries in the pelvic region. The second part of the project focuses on locating the source of hemorrhage and its segmentation. The hemorrhage is segmented using a novel rule based hemorrhage segmentation approach. This approach segments hemorrhage through hemorrhage matching, rule optimization, and region growing. Later the position of hemorrhage in the image and the volume of the hemorrhage are determined to analyze hemorrhage severity. The third part of the project is to automatically classify the outcome using features extracted from the medical images and patient medical records and demographics. A multi-stage feature selection algorithm is used to select the predominant features among all the features. Finally, boosted logistic model tree is used to classify the outcome. The methods are tested on CT images of traumatic pelvic injury patients. The hemorrhage segmentation and classification results seem promising and demonstrate that the proposed method is not only capable of automatically segmenting hemorrhage and classifying outcome, but also has the potential to be used for clinical applications. Finally, the project is extended to abdominal trauma and a novel knowledge based heuristic technique is used to detect and segment spleen from the abdominal CT images. This technique is tested on a limited number of subjects and the results are promising

Fracture Detection in Traumatic Pelvic CT Images

Fracture detection in pelvic bones is vital for patient diagnostic decisions and treatment planning in traumatic pelvic injuries. Manual detection of bone fracture from computed tomography (CT) images is very challenging due to low resolution of the images and the complex pelvic structures. Automated fracture detection from segmented bones can significantly help physicians analyze pelvic CT images and detect the severity of injuries in a very short period. This paper presents an automated hierarchical algorithm for bone fracture detection in pelvic CT scans using adaptive windowing, boundary tracing, and wavelet transform while incorporating anatomical information. Fracture detection is performed on the basis of the results of prior pelvic bone segmentation via our registered active shape model (RASM). The results are promising and show that the method is capable of detecting fractures accurately

Bone segmentation and 3D visualization of CT images for traumatic pelvic injuries

Pelvic bone segmentation is a vital step in analyzing pelvic CT images, which assists physicians with diagnostic decision making in cases of traumatic pelvic injuries. Due to the limited resolution of the original CT images and the complexity of pelvic structures and their possible fractures, automatic pelvic bone segmentation in multiple CT slices is very difficult. In this study, an automatic pelvic bone segmentation approach is proposed using the combination of anatomical knowledge and computational techniques. It is developed for solving the problem of accurate and efficient bone segmentation using multiple consecutive pelvic CT slices obtained from each patient. Our proposed segmentation method is able to handle variation of bone shapes between slices there by making it less susceptible to inter‐personal variability between different patients' data. Moreover, the designed training models are validated using a cross‐validation process to demonstrate the effectiveness. The algorithm's capability is tested on a set of 20 CT data sets. Successful segmentation results and quantitative evaluations are present to demonstrate the effectiveness and robustness of proposed algorithm, well suited for pelvic bone segmentation purposes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106095/1/ima22076.pd

The blast pelvis

Decreasing the human cost of war is a vital role within the Ministry of Defence, and the Defence Medical Services. With the considerable improvements in care, from point of wounding to rehabilitation, it is possible that we have reached the ceiling of optimal management with available, deployed resources. Injury prevention or mitigation may therefore have a more important role than ever in improving survival rates. The current character of conflict, and certainly the recent conflicts in Iraq and Afghanistan have seen the Improvised Explosive Device used to devastating effect to personnel. These devices cause multisystem injuries, and have a high fatality. The lower extremity was most often affected in these recent conflicts, and many fatalities occurred. A greater understanding of lower extremity trauma biomechanics is likely to be key to preventing future fatalities due to injuries in this body region. This thesis focusses on lower extremity blast injury, performs a review of current understanding, and undertakes a casualty data analysis to further understand injury patterns and the cause of fatal wounding. This analysis finds that haemorrhage secondary to pelvic fracture is the key factor in fatal lower extremity injuries, and therefore an area of considerable research interest. Pelvic injury patterns were therefore analysed using measurement techniques to qualify injury patterns and understand the link between injury patterns and the presence of vascular injury. Subsequent physical and computational testing provided a platform to apply different loading conditions to the pelvis to replicate a blast injury, and understand the behaviour of the bony structures under high rate axial loading. This thesis concludes that the anterior pelvic ring at the pubic symphysis is key to pelvic integrity at high rates of loading. Disruption of the anterior pelvis can lead to subsequent posterior ligamentous rupture which, due to the proximity to major vessels, can lead to major haemorrhage and death. Preventing lateral disruption may be the key to maintaining pelvic integrity at these high loading rates, and preventing vascular compromise and fatality from lower extremity blast injuries.Open Acces

Segmentation and Fracture Detection in X-ray images for Traumatic Pelvic Injury

Due to the risk of complications such as hemorrhage, severe pelvic trauma is associated with a high mortality rate. Prompt medical treatment is therefore vital. However, the complexity of the injuries can make successful diagnosis and treatment challenging. By generating predictions and recommendations based on patient data, computer-aided decision support systems have the potential to assist physicians in improving outcomes. However, no current system considers features automatically extracted from medical images. This dissertation describes a system to extract diagnostic features from pelvic X-ray images that can be used as input to the prediction process; specifically, the presence of fracture and quantitative measures of displacement. Feature extraction requires prior identification of separate structures of interest within the pelvis. The proposed system therefore incorporates a hierarchical segmentation algorithm which is able to automatically extract multiple structures in a single pass, using a combination of anatomical knowledge and computational techniques such as directed Hough Transform. This algorithm also applies a novel Spline/ASM segmentation method which combines cubic spline interpolation with a deformable model approach which maintains curved contours and provides local control over segmentation. In order for the proposed system to be used as a component in a computerized decision support system, segmentation is designed to be entirely automatic. Furthermore, Spline/ASM is suitable for many other segmentation applications where the objects of interest show curved contours. After successful segmentation, fracture detection is performed on the pelvic ring and pubis structures, using an algorithm based on wavelet transform, anatomical information and boundary tracing. A method is also developed to calculate quantitative measures of symphysis pubis displacement that may indicate pelvic instability and prove useful in identifying fracture patterns. Finally, X-ray features are combined with patient demographics and physiological scores for generation of predictive rules for injury severity, with promising current results. This indicates the potential diagnostic value of the extracted features, and in turn the usefulness of the proposed radiograph analysis component in a larger decision support system

Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries

Automated hemorrhage detection and segmentation in traumatic pelvic injuries is vital for fast and accurate treatment decision making. Hemorrhage is the main cause of deaths in patients within first 24 hours after the injury. It is very time consuming for physicians to analyze all Computed Tomography (CT) images manually. As time is crucial in emergence medicine, analyzing medical images manually delays the decision-making process. Automated hemorrhage detection and segmentation can significantly help physicians to analyze these images and make fast and accurate decisions. Hemorrhage segmentation is a crucial step in the accurate diagnosis and treatment decision-making process. This paper presents a novel rule-based hemorrhage segmentation technique that utilizes pelvic anatomical information to segment hemorrhage accurately. An evaluation measure is used to quantify the accuracy of hemorrhage segmentation. The results show that the proposed method is able to segment hemorrhage very well, and the results are promising