Evaluation of Elastic Fibers Pattern with Orcein Staining in Differential Diagnosis of Lichen Planopilaris and Discoid Lupus Erythematosus

Differential diagnosis of lichen planopilaris and discoid lupus erythematosus especially in late stages is a problem for clinicians and pathologists. Our aim was to find discriminator histopathologic findings that help us to achieve definite diagnosis without using immunofluorescence study. The histopathologic findings in 77 cases of lichen planopilaris were compared with those of 26 cases of discoid lupus erythematosus with Hematoxylin & Eosin and especially staining (Alcian blue pH 2.5, Periodic Acid Shiff, Orcein). Final histopathologic diagnosis was based on histologic findings, clinicopathological correlation, past medical history and immunofluorescence studies if were applied before. Then elastic fibers pattern in dermis and follicular sheath with orcein staining were described without having information about final diagnosis. New and subtle presentations of histologic changes were assessed. We compared all histopathologic finding for each staining method. Some histologic changes such as hypergranulosis, epidermal atrophy, mucin deposition, diffuse scar and some other patterns were not specific for any diagnosis. A setting of histopathologic findings and clinicopathological correlation were needed for accurate diagnosis. We had only one specimen for the vertical section, and we had no horizontal sections. Description of elastic fibers pattern in orcein staining may be helpful in achieving a specific diagnosis, but this is not completely reliable, and we had overlap features. Finally, immunofluorescence study may be recommended for suspicious cases

Gland segmentation in histopathology images using deep networks and handcrafted features

Histopathology images contain essential information for medical diagnosis and prognosis of cancerous disease. Segmentation of glands in histopathology images is a primary step for analysis and diagnosis of an unhealthy patient. Due to the widespread application and the great success of deep neural networks in intelligent medical diagnosis and histopathology, we propose a modified version of LinkNet for gland segmentation and recognition of malignant cases. We show that using specific handcrafted features such as invariant local binary pattern drastically improves the system performance. The experimental results demonstrate the competency of the proposed system against the state-of-the-art methods. We achieved the best results in testing on section B images of the Warwick-QU dataset and obtained comparable results on section A images