Intelligent image retrieval based on radiology reports.

OBJECTIVES: To create an advanced image retrieval and data-mining system based on in-house radiology reports. METHODS: Radiology reports are semantically analysed using natural language processing (NLP) techniques and stored in a state-of-the-art search engine. Images referenced by sequence and image number in the reports are retrieved from the picture archiving and communication system (PACS) and stored for later viewing. A web-based front end is used as an interface to query for images and show the results with the retrieved images and report text. Using a comprehensive radiological lexicon for the underlying terminology, the search algorithm also finds results for synonyms, abbreviations and related topics. RESULTS: The test set was 108 manually annotated reports analysed by different system configurations. Best results were achieved using full syntactic and semantic analysis with a precision of 0.929 and recall of 0.952. Operating successfully since October 2010, 258,824 reports have been indexed and a total of 405,146 preview images are stored in the database. CONCLUSIONS: Data-mining and NLP techniques provide quick access to a vast repository of images and radiology reports with both high precision and recall values. Consequently, the system has become a valuable tool in daily clinical routine, education and research. KEY POINTS: Radiology reports can now be analysed using sophisticated natural language-processing techniques. Semantic text analysis is backed by terminology of a radiological lexicon. The search engine includes results for synonyms, abbreviations and compositions. Key images are automatically extracted from radiology reports and fetched from PACS. Such systems help to find diagnoses, improve report quality and save time.

A device for simultaneous live cell imaging during uni-axial mechanical strain or compression.

Mechanical stimuli control multiple cellular processes such as secretion, growth, and differentiation. A widely used method to investigate cell strain ex vivo is stretching an elastic membrane to which cells adhere. However, simultaneous imaging of dynamic signals from single living cells grown on elastic substrates during uni-axial changes of cell length is usually hampered by the movement of the sample along the strain axis out of the narrow optical field of view. We used a thin, prestrained, elastic chamber as growth substrate for the cells and deformed the chamber with a computer-controlled stretch device. An algorithm that compensates the lateral displacement during stretch kept any selected point of the whole chamber at a constant position on the microscope during strain or relaxation (compression). Adherent cells or other materials that adhere to the bottom of the chamber at any given position could be imaged during controlled positive (stretch) or negative (compression) changes of cell length. The system was tested on living alveolar type II cells, in which mechanical effects on secretion have been intensively investigated in the past.

Differentiating locally recurrent rectal cancer from scar tissue: Value of diffusion-weighted MRI.

OBJECTIVES: To determine a cut-off apparent diffusion coefficient (ADC) value distinguishing local recurrence from scar tissue in patients with rectal cancer treated with complete surgical tumour removal. METHODS: 72 patients were retrospectively included. Patients underwent 1.5T MRI including multiplanar T2-weighted turbo-spin-echo sequences (TSE) and axial single-shot epi-diffusion-weighted sequences (EPSE). Two independent observers measured mean tumour and scar tissue ADCs by manually drawing regions of interest (ROIs). The t-test and ROC analysis were used for comparison and determining an optimal discrimination threshold. As reference standard histopathological results were used in 23 patients (32%) and clinical follow-up in 49 patients (68%). RESULTS: Recurrent rectal cancer was found in 30 patients (4 female, 26 male, median age 63.13 years) and treatment related changes such as scar tissue in 42 patients (11 female, 31 male, median age 63.67 years). The mean ADC value of tumour recurrence was 1.02×10(-3)mm(2)/s (0.63-1.44×10(-3)mm(2)/s) and of scar tissue 1.77×10(-3)mm(2)/s (1.11-2.41×10(-3)mm(2)/s) showing a statistically significant difference (p<0.001). The cut-off ADC value was 1.34×10(-3)mm(2)/s with a sensitivity, specificity, and accuracy of 93%, 91%, and 92% respectively. CONCLUSIONS: Diffusion weighted MRI allows for the differentiation of tumour recurrence from scar tissue after surgical resection of rectal cancer.