Robust Detection for Red Blood Cells in Thin Blood Smear Microscopy Using Deep Learning

Transcription

1 Robust Detection for Red Blood Cells in Thin Blood Smear Microscopy Using Deep Learning By Yasmin Kassim PhD Candidate in University of Missouri-Columbia Supervised by Dr. Kannappan Palaniappan Mentored in LHNCBC by Dr. Stefan Jaeger

2 Outline Data set and challenges Traditional methods Deep learning object detection approaches Proposed pipeline Examples of cell detection Experiments details Evaluation Experimental results Conclusion and future work

3 Data Set Polygon set, 33 patients, 165 images Points set, 160 patients, 800 images firefly.cs.missouri.edu

4 Challenges Color variations and illumination Cells shapes and appearance Touching cells Staining artifacts Chittagong Medical College & Hospital in Bangladesh

5 Traditional Methods Intensity thresholding and morphological operations Watershed Level Set

6 Deep Learning for Object Detection and Classification

7 Deep learning Detection Techniques R-CNN YOLO Fast R-CNN Faster R-CNN

8 Faster RCNN Architecture 1. CNN (Convolutional Neural Network) 2. RPN (Regional Proposal Network) 3. Fast R-CNN

9 The Proposed Pipeline

10 Examples of Cell Detection Original Image superimposed with bounding boxes of FRCNN prediction One Connected Component (CP) Labeled Image A. Small CP B. Medium CP C. Large CP

11 Experiment 1 for Polygons Set Polygon set, 165 images, 33 patients Train 30 patients (150 images) Total of experiments in experiment 1 is 11 Resize the images to 0.3 it s original size to be Test 3 patients (15 images) FRCNN took 20 epochs 3750 tiles for each experiment

12 Experiment 2 for Points Set Points set, 800 images, 160 patients Train 33 patients (165 images) Train polygon set to test points set Test 160 (800 images) Resize the images to 0.3 it s original size to be FRCNN took 20 epochs 4125 tiles for this experiment

13 Examples on Our Detection

14 Examples on Our Detection

15 Examples on Our Detection

16 Examples on Our Detection

17 Evaluation One point Two or more points TP TP n No Points Remaining points FP FN TP n : The nearest point is the true positive and the other point will be later either as TP if there is a detection around it or just left as FN if there is no detection

18 Evaluation Equations

19 Experimental Results Polygon set, 33 patients, 165 images Method Final comparison F1 Pre Recall STD Unet + FRCNN Segnet + FRCNN Level set Watershed

20 Experimental Results Point set, 160 patients, 800 images Method Final comparison F1 Pre Recall STD Unet + FRCNN Segnet + FRCNN Level set Watershed

21 Evaluation

22 Evaluation

23 Conclusion and Future Work We propose an automated pipeline for detecting RBCs in thin blood smear microscopy using the power of FRCNN and UNet segmentation. Our pipeline architecture provides a more accurate cell detection than other approaches because a foreground mask guides the prediction, which leads to a notably higher true positive rate. Our cell detection pipeline implements a crucial step towards automated malaria diagnosis. Future work will combine our cell detection pipeline with a cell classifier that can differentiate between infected and uninfected cells.