Tianxia Gong

Anterior segment optical coherence tomography parameters in subtypes of primary angle closure.

PURPOSE To compare anterior segment parameters, assessed by anterior segment optical coherence tomography (ASOCT), in subjects categorized as primary angle closure suspect (PACS), primary angle closure (PAC), primary angle closure glaucoma (PACG), and previous acute PAC (APAC); and to identify factors associated with APAC. METHODS This was a prospective ASOCT study of 425 subjects with angle closure (176 PACS, 66 PAC, 125 PACG, and 58 APAC). Customized software was used to measure ASOCT parameters, including angle opening distance (AOD750), trabecular-iris space area (TISA750), anterior chamber depth, width, area and volume (ACD, ACW, ACA, ACV), iris thickness (IT750), iris area (IAREA), and lens vault (LV). Mean differences in anterior segment parameters were evaluated by analysis of covariance (ANCOVA) adjusted for age, sex, and pupil diameter (PD). RESULTS Comparison among the different subgroups showed that ACD, ACA, and ACV were smallest, and IT750 thickest in the APAC group compared with the other subgroups (P < 0.001). LV was greatest in the APAC group (1218 ± 34 μm) followed by PAC (860 ± 31 μm), PACG (845 ± 23 μm), and PACS (804 ± 19 μm), respectively (P = <0.001). While the APAC group had the narrowest angles, the PACS group had the widest (P < 0.001 for both AOD750 and TISA750). Logistic regression showed that greater LV (P = <0.001), narrower TISA750 (P = <0.001), and thicker IT750 (P = 0.007) were the major determinants of APAC. CONCLUSIONS Eyes with APAC had the narrowest angles, smallest anterior segment dimensions, thickest iris, and largest LV compared with PACS, PAC, and PACG. LV, TISA750, and IT750 were the major determinants of APAC.

Subgrouping of Primary Angle-Closure Suspects Based on Anterior Segment Optical Coherence Tomography Parameters

PURPOSE To identify subgroups of primary angle-closure suspects (PACS) based on anterior segment optical coherence tomography (AS-OCT) and biometric parameters. DESIGN Cross-sectional study. PARTICIPANTS We evaluated 243 PACS subjects in the primary group and 165 subjects in the validation group. METHODS Participants underwent gonioscopy and AS-OCT (Carl Zeiss Meditec, Dublin, CA). Customized software (Zhongshan Angle Assessment Program, Guangzhou, China) was used to measure AS-OCT parameters. An agglomerative hierarchical clustering method was first used to determine the optimum number of parameters to be included in the determination of subgroups. The best number of subgroups was then determined using Akaike Information Criterion (AIC) and Gaussian Mixture Model (GMM) methods. MAIN OUTCOME MEASURES Subgroups of PACS. RESULTS The mean age of the subjects was 64.8 years, and 65.02% were female. After hierarchical clustering, 1 or 2 parameters from each cluster were chosen to ensure representativeness of the parameters and yet keep a minimum of redundancy. The parameters included were iris area, anterior chamber depth (ACD), anterior chamber width (ACW), and lens vault (LV). With the use of GMM, the optimal number of subgroups as given by AIC was 3. Subgroup 1 was characterized by a large iris area, subgroup 2 was characterized by a large LV and a shallow ACD, and subgroup 3 was characterized by elements of both subgroups 1 and 2. The results were replicated in a second independent group of 165 PACS subjects. CONCLUSIONS Clustering analysis identified 3 distinct subgroups of PACS subjects based on AS-OCT and biometric parameters. These findings may be relevant for understanding angle-closure pathogenesis and management.

Text Mining in Radiology Reports

Medical text mining has gained increasing interest in recent years. Radiology reports contain rich information describing radiologistpsilas observations on the patientpsilas medical conditions in the associated medical images. However, as most reports are in free text format, the valuable information contained in those reports cannot be easily accessed and used, unless proper text mining has been applied. In this paper, we propose a text mining system to extract and use the information in radiology reports. The system consists of three main modules: a medical finding extractor, a report and image retriever, and a text-assisted image feature extractor. In evaluation, the overall precision and recall for medical finding extraction are 95.5% and 87.9% respectively, and for all modifiers of the medical findings 88.2% and 82.8% respectively. The overall result of report and image retrieval module and text-assisted image feature extraction module is satisfactory to radiologists.

A Semantic Similarity Language Model to Improve Automatic Image Annotation

In recent years, with the rapid proliferation of digital images, the need to search and retrieve the images accurately, efficiently, and conveniently is becoming more acute. Automatic image annotation with image semantic content has attracted increasing attention, as it is the preprocess of annotation based image retrieval which provides users accurate, efficient, and convenient image retrieval with image understanding. Different machine learning approaches have been used to tackle the problem of automatic image annotation; however, most of them focused on exploring the relationship between images and annotation words and neglected the relationship among the annotation words. In this paper, we propose a framework of using language models to represent the word-to-word relation and thus to improve the performance of existing image annotation approaches utilizing probabilistic models. We also propose a specific language model - the semantic similarity language model to estimate the semantic similarity among the annotation words so that annotations that are more semantically coherent will have higher probability to be chosen to annotate the image. To illustrate the general idea of using language model to improve current image annotation systems, we added the language model on top of the two specific image annotation models - the translation model (TM) and the cross media relevance model (CMRM). We tested the improved models on a widely used image annotation corpus - the Corel 5K dataset. Our results show that by adding the semantic similarity language model, the performance of image annotation improves significantly in comparison with the original models. Our proposed language model can also be applied to other image annotation approaches using word probability conditioned on image or word-image joint probability as well.

Myopia in Asian Subjects with Primary Angle Closure: Implications for Glaucoma Trends in East Asia

PURPOSE To evaluate the occurrence of myopia in Asian subjects with angle closure and to assess the ocular biometric parameters in these subjects. DESIGN Cross-sectional study. PARTICIPANTS We prospectively recruited 427 angle-closure subjects (143 primary angle-closure suspects, 75 patients with primary angle closure, 165 patients with primary angle-closure glaucoma, and 44 patients with acute primary angle closure) from a Singapore hospital. METHODS Refractive status was derived from the spherical equivalent of autorefraction. A-scan biometry (Nidek Echoscan Ultrasound US-800; Nidek Co., Tokyo, Japan) was performed to obtain anterior chamber depth (ACD), axial length (AL), lens thickness, and vitreous cavity length (VL). Anterior segment optical coherence tomography was performed to measure lens vault. MAIN OUTCOME MEASURES Refractive status was categorized as myopia (≤-0.50 diopter [D]), emmetropia (-0.50 to +0.50 D), and hyperopia (≥+0.50 D). RESULTS The mean age ± standard deviation of study subjects was 65.6 ± 7.6 years, with most being Chinese (n = 394; 92.3%) and women (n = 275; 64.4%). Overall, myopia was present in 94 subjects (22%), hyperopia was present in 222 subjects (52%), and emmetropia was present in 111 subjects (26%). Of the 94 myopic angle-closure patients, 28 (29.8%) were categorized as having moderate myopia (≤-2.0 to -5.0 D) and 11 (11.7%) were categorized as having high myopia (≤-5.00 D). Although myopic angle-closure subjects had longer ALs (P<0.001) and VLs (P = 0.001) than their emmetropic and hyperopic counterparts, there were no significant differences in ACD (P = 0.77), lens thickness (P = 0.44), or lens vault (P = 0.053). CONCLUSIONS Almost one quarter of angle-closure patients were myopic. Myopic angle-closure subjects had longer VLs and ALs, but there was no difference in ACD. With the increasing rate of myopia in many East Asian populations, there may be many subjects with axial myopia but shallow ACD and angle closure. The implication is that ophthalmologists should not assume that glaucoma patients who are myopic have open angles.

Automatic labeling and classification of brain CT images

Automatic medical image classification is difficult because of the lacking of training data. As manual labeling is too costly, we provide an automatic labeling solution to this problem by making use of the radiology report associated with the medical images. We first segment and reconstruct the 3D regions of interest (ROIs) from the medical images, and extract pathology and anatomy information from the associated report. We use an anatomical atlas to map the ROIs to the anatomy part(s) and match the pathology information of the same anatomy part(s) from the text. In this way, the ROIs are automatically labeled with pathology types which can be served as class labels, and a training data set of a large number of training instances is generated automatically. We extract the volume, color, location, and shape features of the ROIs, and classify the types of ROIs using these features. The overall evaluation result is promising to doctors and medical professionals. Our experiment is conducted using traumatic brain injury CT images; however, our framework of automatically labeling and classifying medical cases can be extended to medical images in other modality or of other anatomical part.

TBIdoc: 3D content-based CT image retrieval system for traumatic brain injury

Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction. However, current hospital database system does not provide an efficient and intuitive tool for doctors to search out cases relevant to the current study case. In this paper, we present the TBIdoc system: a content-based image retrieval (CBIR) system which works on the TBI CT images. In this web-based system, user can query by uploading CT image slices from one study, retrieval result is a list of TBI cases ranked according to their 3D visual similarity to the query case. Specifically, cases of TBI CT images often present diffuse or focal lesions. In TBIdoc system, these pathological image features are represented as bin-based binary feature vectors. We use the Jaccard-Needham measure as the similarity measurement. Based on these, we propose a 3D similarity measure for computing the similarity score between two series of CT slices. nDCG is used to evaluate the system performance, which shows the system produces satisfactory retrieval results. The system is expected to improve the current hospital data management in TBI and to give better support for the clinical decision-making process. It may also contribute to the computer-aided education in TBI.

Anterior segment imaging-based subdivision of subjects with primary angle-closure glaucoma

PurposeThe purpose of this study was to identify whether it was possible to subdivide subjects with primary angle-closure glaucoma (PACG) based on anterior segment optical coherence tomography (ASOCT) imaging, and to determine the characteristics of such subgroups.MethodsWe evaluated 210 subjects with PACG. All subjects underwent gonioscopy and ASOCT imaging. Customized software was used to measure ASOCT parameters. An agglomerative hierarchical clustering method was first used to determine the optimum number of parameters to be included in the determination of subgroups. Then, the best number of subgroups was determined using Akaike Information Criterion (AIC) and Gaussian Mixture Model (GMM) methods.ResultsThe mean age of the subjects was 67.9 years, and 53.3% were female. Following the hierarchical clustering, four parameters (iris area, anterior chamber depth (ACD), anterior chamber width (ACW), and lens vault (LV)) were chosen to be representative of related parameters. The optimal number of subgroups using GMM analysis and AIC was 3. Subgroup 1 (N=89; 42.4%) was characterized by a large iris area, subgroup 2 (N=24; 11.4%) by a large LV and a shallow ACD, whereas subgroup 3 (N=97; 46.2%) displayed only intermediate values across iris area, LV, and ACD.ConclusionsWe identified three distinct subgroups of PACG subjects based on ASOCT imaging.

MiBank: A web-based integrated medical information system for traumatic brain injury

Approximately ten million people in the world suffer from traumatic brain injury (TBI) each year. A total of

Automatic Pathology Annotation on Medical Images: A Statistical Machine Translation Framework

60 billion cost due to TBI was estimated in the United States in year 2000. To reduce the burden more clinical research and education are required. In this study we developed MiBank, a web-based integrated TBI information system, to enable rapid access to both digital images and associated text reports for audit, education and research. MiBank contains more than 30,000 brain computed tomography (CT) images from over 500 patients and is equipped with functional options to search, compare, summarize and annotate CT images, radiology reports and clinician remarks online. The image annotation function is designed to enable clinicians and researchers to capture and display domain expert knowledge, and a discussion forum function encourages active communication and sharing. Emphasizing confidentiality of anonymised data and access control, MiBank provides a virtual collaboration platform integrating various clinical data sets for research and continuing education. As an online information system, it eliminates the restrictions of the traditional isolated DICOM workstations. MiBank can potentially support remote consulting and statistical analysis of aggregated multimodality data. Although MiBank is designed and implemented for TBI, it may be extended and customized to study other clinical disorders. In this report, we share our learning experience through user survey and also propose a future plan to improve the system. MiBank may be accessible by researchers and clinicians on request.