Automatic Detection of Arrow Annotation Overlays in Biomedical Images

Images in biomedical articles are often referenced for clinical decision support, educational purposes, and medical research. Authors-marked annotations such as text labels and symbols overlaid on these images are used to highlight regions of interest which are then referenced in the caption text or figure citations in the articles. Detecting and recognizing such symbols is valuable for improving biomedical information retrieval. In this research, image processing and computational intelligence methods are integrated for object segmentation and discrimination and applied to the problem of detecting arrows on these images. Evolving Artificial Neural Networks (EANNs) and Evolving Artificial Neural Network Ensembles (EANNEs) computational intelligence-based algorithms are developed to recognize overlays, specifically arrows, in medical images. For these discrimination techniques, EANNs use particle swarm optimization and genetic algorithm for artificial neural network (ANN) training, and EANNEs utilize the number of ANNs generated in an ensemble and negative correlation learning for neural network training based on averaging and Linear Vector Quantization (LVQ) winner-take-all approaches. Experiments performed on medical images from the imageCLEFmed\u2708 data set, yielded area under the receiver operating characteristic curve and precision/recall results as high as 0.988 and 0.928/0.973, respectively, using the EANNEs method with the winner-take-all approach

Histological outcome during long-term lamivudine therapy

Background & Aims: One year of lamivudine for chronic hepatitis B results in histologic improvement. We aimed to assess the histological impact of longer-term treatment. Methods: Sets of 3 liver biopsies, from 63 patients before and after 1 year of randomized lamivudine treatment and after 2 years of further open-label treatment, were assigned Histologic Activity Index scores under code. Results: At the end of year 1, 36/63 (57%) showed ≥2 point improvement and 24/63 (38%) no change in necroinflammatory activity; after 2 additional years of lamivudine, 38/63 (60%) remained stable and 12/63 (19%) continued to improve. Worsening occurred in similar proportions of patients with and without YMDD (tyrosine, methionine, aspartate, aspartate) variants. After all 3 years of lamivudine treatment, 35/63 (56%) of patients showed improvement, 21/63 (33%) no change, and 7/63 (11%) worsening. Those without, compared with those with, YMDD variants were more likely to improve (17/22 [77%] vs. 18/41 [44%]) and less likely to deteriorate (1/22 [5%] vs. 6/41 [15%]). Patients with YMDD variants for >2 years were least likely to improve (8/22 [36%]). Bridging fibrosis improved by ≥1 level in 12/19 (63%), and cirrhosis improved (score of 4 to ≤3) in 8/11 (73%). Only 1/52 [2%]) showed progression to cirrhosis, and 3/34 (9%) showed progression to bridging fibrosis (all with YMDD variants). Conclusions: Three years of lamivudine therapy reduces necroinflammatory activity and reverses fibrosis (including cirrhosis) in most patients. The emergence of YMDD variants blunts histologic responses; therefore, extended-duration YMDD variants may require additional therapies to maintain the histological benefit of treatment. GASTROENTEROLOGY 2003;124:105-11