Efficient FPGA Implementation of Automatic Nuclei Detection in Histopathology Images

Area of Science:

• Digital Pathology
• Computer-Aided Diagnosis
• Hardware Acceleration

Background:

• Accurate cell nuclei detection is crucial for pathology-based Computer Aided Diagnosis (CADx).
• High-resolution histopathology images (gigapixels) make nuclei detection computationally intensive and time-consuming.
• Real-time analysis requires specialized hardware accelerators to reduce processing bottlenecks.

Purpose of the Study:

• To propose and implement an automated nuclei detection algorithm on a Field Programmable Gate Array (FPGA).
• To accelerate the processing of large histopathology images for real-time diagnostic assistance.
• To evaluate the trade-off between processing speed and detection accuracy.

Main Methods:

• Development of an automated nuclei detection algorithm utilizing generalized Laplacian of Gaussian (LoG) filters.
• Implementation of the algorithm on a Field Programmable Gate Array (FPGA) for hardware acceleration.
• Experimental validation of the FPGA implementation using histopathology image datasets.

Main Results:

• The FPGA implementation demonstrates a significant reduction in nuclei detection processing time.
• The proposed architecture maintains high detection accuracy comparable to software-based methods.
• The system shows potential for real-time performance in pathological image analysis.

Conclusions:

• FPGA-based hardware acceleration offers a viable solution for efficient nuclei detection in digital pathology.
• The developed system can assist pathologists by providing rapid and accurate diagnostic insights.
• This approach enhances the feasibility of real-time, pathology-based Computer Aided Diagnosis systems.