Methods Inf Med - A fast, automatic segmentation algorithm for locating and delineating touching cell boundaries in imaged histopathology.

Tópicos

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Resumo

CKGROUND: Automated analysis of imaged histopathology specimens could potentially provide support for improved reliability in detection and classification in a range of investigative and clinical cancer applications. Automated segmentation of cells in the digitized tissue microarray (TMA) is often the prerequisite for quantitative analysis. However overlapping cells usually bring significant challenges for traditional segmentation algorithms.OBJECTIVES: In this paper, we propose a novel, automatic algorithm to separate overlapping cells in stained histology specimens acquired using bright-field RGB imaging.METHODS: It starts by systematically identifying salient regions of interest throughout the image based upon their underlying visual content. The segmentation algorithm subsequently performs a quick, voting based seed detection. Finally, the contour of each cell is obtained using a repulsive level set deformable model using the seeds generated in the previous step. We compared the experimental results with the most current literature, and the pixel wise accuracy between human experts' annotation and those generated using the automatic segmentation algorithm.RESULTS: The method is tested with 100 image patches which contain more than 1000 overlapping cells. The overall precision and recall of the developed algorithm is 90% and 78%, respectively. We also implement the algorithm on GPU. The parallel implementation is 22 times faster than its C/C++ sequential implementation.CONCLUSION: The proposed segmentation algorithm can accurately detect and effectively separate each of the overlapping cells. GPU is proven to be an efficient parallel platform for overlapping cell segmentation.

Resumo Limpo

ckground autom analysi imag histopatholog specimen potenti provid support improv reliabl detect classif rang investig clinic cancer applic autom segment cell digit tissu microarray tma often prerequisit quantit analysi howev overlap cell usual bring signific challeng tradit segment algorithmsobject paper propos novel automat algorithm separ overlap cell stain histolog specimen acquir use brightfield rgb imagingmethod start systemat identifi salient region interest throughout imag base upon under visual content segment algorithm subsequ perform quick vote base seed detect final contour cell obtain use repuls level set deform model use seed generat previous step compar experiment result current literatur pixel wise accuraci human expert annot generat use automat segment algorithmresult method test imag patch contain overlap cell overal precis recal develop algorithm respect also implement algorithm gpu parallel implement time faster cc sequenti implementationconclus propos segment algorithm can accur detect effect separ overlap cell gpu proven effici parallel platform overlap cell segment

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