Details

Autor(en) / Beteiligte

• Xie, Yuanpu
• Xing, Fuyong
• Shi, Xiaoshuang
• Kong, Xiangfei
• Su, Hai
• Yang, Lin

Titel

Efficient and robust cell detection: A structured regression approach

Ist Teil von

• Medical image analysis, 2018-02, Vol.44, p.245-254

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2018

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• Highllights•A highly efficient and effective fully residual convolutional neural network is proposed for cell detection.•We validate the superiority of structured regression over the conventional pixel wise classification method for cell detection.•We prove the robustness and generalization capability of our model using four datasets, each corresponding to a distinct staining method or image modality. [Display omitted] Efficient and robust cell detection serves as a critical prerequisite for many subsequent biomedical image analysis methods and computer-aided diagnosis (CAD). It remains a challenging task due to touching cells, inhomogeneous background noise, and large variations in cell sizes and shapes. In addition, the ever-increasing amount of available datasets and the high resolution of whole-slice scanned images pose a further demand for efficient processing algorithms. In this paper, we present a novel structured regression model based on a proposed fully residual convolutional neural network for efficient cell detection. For each testing image, our model learns to produce a dense proximity map that exhibits higher responses at locations near cell centers. Our method only requires a few training images with weak annotations (just one dot indicating the cell centroids). We have extensively evaluated our method using four different datasets, covering different microscopy staining methods (e.g., H & E or Ki-67 staining) or image acquisition techniques (e.g., bright-filed image or phase contrast). Experimental results demonstrate the superiority of our method over existing state of the art methods in terms of both detection accuracy and running time.