A study on the feasibility of active contours on automatic CT bone segmentation

Automatic Segmentation and Measurement on Knee Computerized Tomography Images for Patellar Dislocation Diagnosis

Traditionally, for diagnosing patellar dislocation, clinicians make manual geometric measurements on computerized tomography (CT) images taken in the knee area, which is often complex and error-prone. Therefore, we develop a prototype CAD system for automatic measurement and diagnosis. We firstly segment the patella and the femur regions on the CT images and then measure two geometric quantities, patellar tilt angle (PTA), and patellar lateral shift (PLS) automatically on the segmentation results, which are finally used to assist in diagnoses. The proposed quantities are proved valid and the proposed algorithms are proved effective by experiments.

PET Index of Bone Glucose Metabolism (PIBGM) Classification of PET/CT Data for Fever of Unknown Origin Diagnosis

Objectives

Fever of unknown origin (FUO) remains a challenge in clinical practice. Fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is helpful in diagnosing the etiology of FUO. This paper aims to develop a completely automatic classification method based on PET/CT data for the computer-assisted diagnosis of FUO.

Methods

We retrospectively analyzed the FDG PET/CT scan of 175 FUO patients, 79 males and 96 females. The final diagnosis of all FUO patients was achieved through pathology or clinical evaluation, including 108 normal patients and 67 FUO patients. CT anatomic information was used to acquire bone functional information from PET images. The skeletal system of FUO patients was classified by analyzing the standardized uptake value (SUV) and the PET index of bone glucose metabolism (PIBGM). The SUV distributions in the bone marrow and the bone cortex were also studied in detail.

Results

The SUV and PIBGM of the bone marrow only slightly differed between the FUO patients and normal people, whereas the SUV of whole bone structures and the PIBGM of the bone cortex significantly differed between the normal people and FUO patients. The method detected 43 patients from 67 FUO patients, with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 64.18%, 95%, 93.48%, 72.73%, and 83.33%, respectively.

Conclusion

The experimental results demonstrate that the study can achieve automatic classification of FUO patients by the proposed novel biomarker of PIBGM, which has the potential to be utilized in clinical practice.

Patient-specific finite element modeling of bones

Finite element modeling is an engineering tool for structural analysis that has been used for many years to assess the relationship between load transfer and bone morphology and to optimize the design and fixation of orthopedic implants. Due to recent developments in finite element model generation, for example, improved computed tomography imaging quality, improved segmentation algorithms, and faster computers, the accuracy of finite element modeling has increased vastly and finite element models simulating the anatomy and properties of an individual patient can be constructed. Such so-called patient-specific finite element models are potentially valuable tools for orthopedic surgeons in fracture risk assessment or pre- and intraoperative planning of implant placement. The aim of this article is to provide a critical overview of current themes in patient-specific finite element modeling of bones. In addition, the state-of-the-art in patient-specific modeling of bones is compared with the requirements for a clinically applicable patient-specific finite element method, and judgment is passed on the feasibility of application of patient-specific finite element modeling as a part of clinical orthopedic routine. It is concluded that further development in certain aspects of patient-specific finite element modeling are needed before finite element modeling can be used as a routine clinical tool.

Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression

Boundary extraction of carpal bone images is a critical operation of the automatic bone age assessment system, since the contrast between the bony structure and soft tissue are very poor. In this paper, we present an edge following technique for boundary extraction in carpal bone images and apply it to assess bone age in young children. Our proposed technique can detect the boundaries of carpal bones in X-ray images by using the information from the vector image model and the edge map. Feature analysis of the carpal bones can reveal the important information for bone age assessment. Five features for bone age assessment are calculated from the boundary extraction result of each carpal bone. All features are taken as input into the support vector regression (SVR) that assesses the bone age. We compare the SVR with the neural network regression (NNR). We use 180 images of carpal bone from a digital hand atlas to assess the bone age of young children from 0 to 6 years old. Leave-one-out cross validation is used for testing the efficiency of the techniques. The opinions of the skilled radiologists provided in the atlas are used as the ground truth in bone age assessment. The SVR is able to provide more accurate bone age assessment results than the NNR. The experimental results from SVR are very close to the bone age assessment by skilled radiologists.