Charles Cockrell

Blunt bowel and mesenteric injury: MDCT diagnosis.

Multidetector computed tomography (MDCT) has emerged as the imaging modality of choice for evaluating the abdomen and pelvis in trauma patients. MDCT readily detects injury of the solid organs as well as direct and indirect features of bowel and/or mesenteric injury—an important advance given that unrecognized bowel and mesenteric injuries may result in high morbidity and mortality. Nonetheless, challenges persist in the interpretation of abdominal and pelvic CT images in trauma patients. Difficulty in interpretation may result from lack of familiarity with or misunderstanding of CT features of bowel and/or mesenteric injury. Moreover, due to major technical advances afforded by MDCT, new CT features of bowel and/or mesenteric injuries have been recognized. Beading and termination of mesenteric vessels indicating surgically important mesenteric injury is an example of one of these new features. MDCT also allows for the detection of small or trace amounts of isolated intraperitoneal fluid in trauma patients, although the clinical management of these patients is still controversial. This pictorial essay illustrates the spectrum of typical, atypical, and newly reported MDCT features of bowel and mesenteric injuries due to blunt trauma. The features that help to differentiate these injuries from pitfalls are emphasized in these proven cases.

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.

Unified wavelet and gaussian filtering for segmentation of CT images; application in segmentation of bone in pelvic CT images

BackgroundThe analysis of pelvic CT scans is a crucial step for detecting and assessing the severity of Traumatic Pelvic Injuries. Automating the processing of pelvic CT scans could impact decision accuracy, decrease the time for decision making, and reduce health care cost. This paper discusses a method to automate the segmentation of bone from pelvic CT images. Accurate segmentation of bone is very important for developing an automated assisted-decision support system for Traumatic Pelvic Injury diagnosis and treatment.MethodsThe automated method for pelvic CT bone segmentation is a hierarchical approach that combines filtering and histogram equalization, for image enhancement, wavelet analysis and automated seeded region growing. Initial results of segmentation are used to identify the region where bone is present and to target histogram equalization towards the specific area. Speckle Reducing Anisotropic Didffusion (SRAD) filter is applied to accentuate the desired features in the region. Automated seeded region growing is performed to refine the initial bone segmentation results.ResultsThe proposed method automatically processes pelvic CT images and produces accurate segmentation. Bone connectivity is achieved and the contours and sizes of bones are true to the actual contour and size displayed in the original image. Results are promising and show great potential for fracture detection and assessing hemorrhage presence and severity.ConclusionPreliminary experimental results of the automated method show accurate bone segmentation. The novelty of the method lies in the unique hierarchical combination of image enhancement and segmentation methods that aims at maximizing the advantages of the combined algorithms. The proposed method has the following advantages: it produces accurate bone segmentation with maintaining bone contour and size true to the original image and is suitable for automated bone segmentation from pelvic CT images.

Prostate cancer and its mimics at multiparametric prostate MRI

One in six males will develop prostate cancer during their lifetime. Prostate cancer is the second leading cause of cancer death in American males, behind only lung cancer. Unfortunately, even though this disease is so common, clinical screening methods such as prostate-specific antigen test and transrectal ultrasound-guided prostate biopsy lack sensitivity and specificity in diagnosing prostate cancer. In recent years, multiparametric prostate MRI has emerged as a very important tool in the diagnosis of prostate carcinoma with a high accuracy. However, diagnostic difficulty is often encountered even with an experienced abdominal radiologist. That is mainly because many normal and abnormal entities can mimic prostate carcinoma at multiparametric MRI. Therefore, the purpose of this pictorial review is to discuss the usefulness of multiparametric prostate MRI in the diagnosis of prostate carcinoma, emphasizing the key MRI features that help to make a distinction of prostate carcinoma from its mimics.

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.

A new hierarchical method for multi-level segmentation of bone in pelvic CT scans

Pelvic bone segmentation is a vital step in analyzing pelvic CT images and assisting physicians with diagnostic decisions in traumatic pelvic injuries. A new hierarchical segmentation algorithm is proposed using a template-based best shape matching method and Registered Active Shape Model (RASM) to automatically extract pelvic bone tissues from multi-level pelvic CT images. A novel hierarchical initialization process for RASM is proposed. 449 CT images across seven patients are used to test and validate the reliability and robustness of the proposed method. The segmentation results show that the proposed method performs better with higher accuracy than standard ASM method.

Intracranial pressure level prediction in traumatic brain injury by extracting features from multiple sources and using machine learning methods

This paper proposes a non-intrusive method to predict/estimate the intracranial pressure (ICP) level based on features extracted from multiple sources. Specifically, these features include midline shift measurement and texture features extracted from CT slices, as well as patients demographic information, such as age. Injury Severity Score is also considered. After aggregating features from slices, a feature selection scheme is applied to select the most informative features. Support vector machine (SVM) is used to train the data and build the prediction model. The validation is performed with 10 fold cross validation. To avoid overfitting, all the feature selection and parameter selection are done using training data during the 10 fold cross validation for evaluation. This results an nested cross validation scheme implemented using Rapidminer. The final classification result shows the effectiveness of the proposed method in ICP prediction.

Pictorial review. Diagnosis of recurrent prostate cancer and its mimics at multiparametric prostate MRI.

Biochemical recurrence after treatment for prostate cancer (PCa) is a significant issue. Early diagnosis of local recurrence is important for making prompt treatment decisions and is strongly associated with patient prognosis. Without salvage therapy, the average time from development of local recurrence to distant metastasis is approximately 3 years. Biochemical recurrence does not differentiate local recurrence from systemic disease; there is no reliable way to clinically diagnose local recurrence. Recent advances in multiparametric MRI (mp-MRI) techniques have markedly improved detection of local recurrence following therapy. However, a wide variety of entities can mimic recurrent PCa at mp-MRI. Therefore, the purpose of this pictorial review is to discuss the MRI findings of locally recurrent PCa and its mimics, emphasizing the key MRI features that help to differentiate local recurrence from its mimics.

Automated midline shift and intracranial pressure estimation based on brain CT images

In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.

Detection of fracture and quantitative assessment of displacement measures in pelvic X-RAY images

Fracture detection in cases of traumatic pelvic injuries is crucial for rapid and successful patient treatment. Initial diagnosis is typically made via X-ray images, which can be challenging and time-consuming to analyze due to their low resolution and the differing visual characteristics of fractures by their location. This paper presents a fracture detection method for the pelvic ring based on Discrete Wavelet Transform and boundary tracing applied to windows extracted from the ring, as defined by prior automated region segmentation via a deformable Spline/ASM model. Results so far are promising, and indicate that the approach can extract useful features for a trauma decision-making system to assist physicians and improve patient care.