C. C. Tchoyoson Lim

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.

Giant spinal ganglioneuroma

Ganglioneuromas are rare benign tumours, which may affect any part of the spine and spinal cord. They occasionally grow to a large size but total excision using microsurgical techniques is often possible, and may be curative. This case report illustrates the clinical and histopathological features of two rare giant ganglioneuromas of the spinal cord.

Large artery ischaemic stroke in severe acute respiratory syndrome (SARS).

Abstract.Of the 206 patients who contracted Severe Acute Respiratory Syndrome (SARS) in Singapore five developed large artery cerebral infarctions. Four patients were critically-ill and three died. Intravenous immunoglobulin was given to three patients. An increased incidence of deep venous thrombosis and pulmonary embolism was also observed among the critically-ill patients. We believe our experience warrants an increased vigilance against stroke and other thrombotic complications among critically-ill SARS patients in future outbreaks, especially if treatment such as intravenous immunoglobulin, that increases pro-thrombotic tendency, is contemplated.

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.

From hemorrhage to midline shift: A new method of tracing the deformed midline in traumatic brain injury ct images

In intracranial pathological examinations using CT scan, brain midline shift (MLS) is an important diagnostic feature indicating the pathological severity and patients survival possibility. In this paper, we develop a new method of tracing the brain midline shift in traumatic brain injury (TBI) CT images using its original cause - the hemorrhage. Firstly, we model the relationship between the hemorrhage and the midline deformation caused by it using a linear regression model (H-MLS model). Secondly, using the H-MLS model, the deformed midline is predicted from the hemorrhage detected in CT images. Finally, the predicted deformed midline is adjusted according to the visual symmetry information. Preliminary experiments show that the proposed method is effective and time-efficient.

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.

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 detection and quantification of brain midline shift using anatomical marker model.

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.

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

Brain midline shift (MLS) is a significant factor in brain CT diagnosis. In this paper, we present a new method of automatically detecting and quantifying brain midline shift in traumatic injury brain CT images. The proposed method automatically picks out the CT slice on which midline shift can be observed most clearly and uses automatically detected anatomical markers to delineate the deformed midline and quantify the shift. For each anatomical marker, the detector generates five candidate points. Then the best candidate for each marker is selected based on the statistical distribution of features characterizing the spatial relationships among the markers. Experiments show that the proposed method outperforms previous methods, especially in the cases of large intra-cerebral hemorrhage and missing ventricles. A brain CT retrieval system is also developed based on the brain midline shift quantification results.

Automated Prediction of Glasgow Outcome Scale for Traumatic Brain Injury

Large number of medical images are produced daily in hospitals and medical institutions, the needs to efficiently process, index, search and retrieve these images are great. In this paper, we propose a pathology based medical image annotation framework using a statistical machine translation approach. After pathology terms and regions of interest (ROIs) are extracted from training text and images respectively, we use machine translation model IBM Model 1 to iteratively learn the alignment between the ROIs and the pathology terms and generate an ROI-to-pathology translation table. In testing phase, we annotate the ROI in the image with the pathology label of the highest probability in the translation table. The overall annotation results and the retrieval performance are promising to doctors and medical professionals.