• Journal of Gastric Cancer
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• 제11권2호
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• pp.122-125
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• 2011

We report a rare case of the coexistence of a gastric small cell neuroendocrine carcinoma with a gastric adenocarcinoma. A 62-year-old man presented with epigastric soreness for 1 month. Esophagogastroduodenoscopy revealed a Borrmann type I tumor at the lesser curvature of the lower body of the stomach. The patient underwent a distal gastrectomy with D2 lymph node dissection and the resected specimen exhibited a $3.5{\times}3.5$ cm sized, fungating lesion. Two separated, not intermingling, lesions with non-adenocarcinoma components encircled by well differentiated adenocarcinoma components were identified microscopically. The non-adenocarcinoma component showed neuroendocrine features, such as a solid and trabecular pattern, and the tumor cells showed a high nuclear grade with minimal cytoplasm, indistinct nucleoli, and positive response for synaptophysin, CD56. The final pathological diagnosis was a gastric mixed exocrine-endocrine carcinoma (MEEC) composed of an adenocarcinoma and small cell neuroendocrine carcinoma of the collision type.

• 한국멀티미디어학회논문지
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• 제20권7호
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• pp.979-985
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• 2017

This paper, which covers a fluorescence microscopy image of brain tumor cells, looks at drug reactions by treating different types and concentrations of drugs on a plate of $24{\times}16$ wells. Due to the limitation of the field of view, a well was taken into 9 field images, and each has an overlapping area with its neighboring fields. To analyze more precisely, image stitching is needed. The basic method is finding a similar area using normalized cross-correlation (NCC). The problem is that some overlapping areas may not have any duplicated cells that help to find the matching point. In addition, the cell objects have similar sizes and shapes, which makes distinguishing them difficult. To avoid calculating similarity between blank areas and roughly distinguishing different cells, thresholding is added. The thresholding method classifies background and cell objects based on fixed thresholds and finds the location of the first seen cell. After getting its location, NCC is used to find the best correlation point. The results are compared with a simple boundary stitched image. Our proposed method stitches images that are connected in a grid form without collision, selecting the best correlation point among areas that contain overlapping cells and ones without it.