In the diagnosis of preinvasive breast cancer, some of the intraductal proliferations pose a special challenge. The continuum of intraductal breast lesions includes the usual ductal hyperplasia (UDH), atypical ductal hyperplasia (ADH), and ductal carcinoma in situ (DCIS). The current standard of care is to perform percutaneous needle biopsies for diagnosis of palpable and image-detected breast abnormalities. UDH is considered benign and patients diagnosed UDH undergo routine follow-up, whereas ADH and DCIS are considered actionable and patients diagnosed with these two subtypes get additional surgical procedures. About 250,000 new cases of intraductal breast lesions are diagnosed every year. A conservative estimate would suggest that at least 50% of these patients are needlessly undergoing unnecessary surgeries. Thus improvement in the diagnostic re-producibility and accuracy is critically important for effective clinical management of these patients. In this study, a prototype system for automatically classifying breast microscopic tissues to distinguish between UDH and actionable subtypes (ADH and DCIS) is introduced. This system automatically evaluates digitized slides of tissues for certain cytological criteria and classifies the tissues based on the quantitative features derived from the images. The system is trained using a total of 327 regions of interest (ROIs) collected across 62 patient cases and tested with a sequestered set of 149 ROIs collected across 33 patient cases. An overall accuracy of 87.9% is achieved on the entire test data. The test accuracy of 84.6% obtained with borderline cases (26 of the 33 test cases) only, when compared against the diagnostic accuracies of nine pathologists on the same set (81.2% average), indicates that the system is highly competitive with the expert pathologists as a stand-alone diagnostic tool and has a great potential in improving diagnostic accuracy and reproducability when used as a “second reader” in conjunction with the pathologists.
In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.
This letter presents unsupervised hyperspectralimage classification based on fuzzy-clustering algorithms that spatially exploit membership relations. Not only is the conventional fuzzy c-means approach used to demonstrate the advantage of using membership relations but also Gustafson-Kessel clustering, which uses an adaptive distance norm, is, for the first time, used for the segmentation of hyperspectral images. A novel approach to include spatial information in the segmentation process is achieved by making use of spatial relations of fuzzy-membership functions among neighbor pixels. Two-and three-dimensional Gaussian filtering of fuzzy-membership degrees is utilized for this purpose. A novel phase-correlation-based similarity measure is used to further enhance the performance of the proposed approach by taking spatial relations into account for pixels with similar spectral characteristics only. It is shown that the proposed approach provides superior clustering performance for hyperspectral images.
We investigated the antioxidant and anti-inflammatory effects of propolis on bleomycin induced lung fibrosis and compared these effects to prednisolone treatment. Forty rats were divided into four groups of ten: group 1 was treated with intratracheal infusion of 0.2 ml physiological saline followed by daily treatment with 0.5 ml physiological saline for 20 days. In the remaining groups (groups 2 - 4), 5 mg/kg bleomycin was given via the trachea. Rats in group 2 were given 0.5 ml physiological saline. Rats in group 3 were treated with 100 mg/kg propolis, and 10 mg/kg prednisolone was given to rats in group 4. The treatments for all groups were continued for 20 days. On postoperative day 21, blood and lung samples were taken for biochemistry, histopathology and electron microscopy evaluation. We compared oxidative stress parameters and found lower malondialdehyde and myeloperoxidase levels, and higher total sulfhydryl levels and catalase activities for the bleomycin + propolis group than for the bleomycin and bleomycin + prednisolone groups. The highest mean fibrosis score was detected in the bleomycin group. Although the mean fibrosis scores of the bleomycin + propolis and bleomycin + prednisolone groups were not significantly different, electron microscopy revealed that propolis diminished bleomycin induced lung fibrosis more effectively than prednisolone. The effects of propolis might be due to its potent antioxidant and anti-inflammatory properties.
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