Automated Cell Segmentation and Spot Detection in Fluorescence in Situ Hybridization Staining to Assess HER2 Status in Breast Cancer
Fluorescence in situ hybridization (FISH) approach is constituted of a pair of complementary techniques for precisely detecting gene amplification and over-expression which are regarded as signs of cancer in patients. Signal detection of FISH whole slides is extremely significant as enables to detect amplification situation. However, nuclei detection and segmentation of FISH slides through microscopic images is tedious and time-consuming for pathologists to evaluate. Furthermore, FISH specimen slides provided at pathological laboratories are frequently noisy and not analyzable entirely. Therefore, traditional visual methods require more time due to fact that they are exceedingly reliant on human view. They require more time and attention in the evaluation process by pathologists. Nowadays, computer-aided FISH solutions bring radical remedies in this particular problematic area of pathology. Although computeraided FISH solutions have many advantages, they have some drawbacks due to the variability of staining images. In this study, we present an accurate cell nuclei segmentation and signal detection methodology to detect red and green spots localized in segmented cells. The problems in the visual experiments as well as the assessment of amplification state of the evaluated cases are presented, which corresponds to the visual scoring of pathologists.
biomarker, image processing, cell segmentation, tilebased, processing, fluorescence in situ hybridization (FISH), region, merging.
HER2/neu gene status has become indispensable in pathologic scoring of invasive ductal breast carcinoma. Although FISH is an expensive and time consuming process, it guarantees the result of the case. If the result is positive, Trastuzumab therapy is given to the patient. False treatments with Trastuzumab lead to loss of money and time. It is also essential to be sure that the patient morality is secured by avoiding false treatments. Our proposed algorithm has three advantages. Basically, it includes whole slide processing that lets user control all parts of the slide. It does not require color phase adjustment such as adjustment of brightness and contrast, and it is also very fast and robust due to fact that we divided each slide into tiles, which reduces the memory need.
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