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Presentation-09022015
- 1. Deep Learning for Automatic Cell Detection in Light Microscopy Zebrafish Images Bo Dong bdong2@sheffield.ac.uk
- 2. Intro: Why using Zebrafish • Genetic similarity to humans • Easier to house and care for than rodents • Impact of any genetic mutation or drug treatment is easy to see • Lots of offspring • Easier to introduce genetic changes • Develops very fast (3 days for research)
- 3. Intro: Parkinson’s Disease (PD) • Changes: Nerve cells use a brain chemical called dopamine to help control muscle movement. In Parkinson’s disease, dopamine-producing nerve cells begin to die off, leaving too little of the chemical. Without dopamine, the cells that control movement cannot send messages to the muscles. This makes it hard to control the muscles and causes the muscle tremors symptomatic of Parkinson’s. Slowly, over time, this damage gets worse. • Causes: Until now, the causes of these dopaminergic neurons to waste away remain unknown. SOURCES: Parkinson’s Disease Foundation
- 4. • Phenomenon: Genetic zebrafish model of PD show a reduction of around 25% of their dopaminergic neurons, as early as 3 days post fertilisation, compared with normal model. • Verify: To verify whether scientists’ treatment approaches for healing PD are effective or not, neuroscientists have to count and compare the number of dopaminergic neurons between two types of zebrafish model in large quantities. • Problem: At the moment, counting cells is a manual, time consuming, subjective, and error-prone process. • Challenge: The challenge is to develop a high-throughput method to free neuroscientists for counting dopaminergic neurons automatically in light microscopy images. Zebrafish for PD Research
- 5. Prepare the zebrafish image dataset Recording Through the Microscope Dopaminergic neurons visualisation process (WISH for TH) Wide-field Microscope Dataset: http://www.cistib.com/cistib_shf/index.php/translation/downloads Labeling Dataset: • 35 zebrafish embryo stacks. • 35 .txt files: containing 3D coordinates of all cell- centre pixels. • 25 for training, 10 for testing. • Stack Size: 1024*1344*z. • Spatial resolution: 3µm. • axial resolution: 1.5µm. • 20X magnification. • Numerical aperture: 0.7
- 6. Overall Structure of our method 1. Colour Normalization 2. Cell Region Detector 3. Cell Pixel Detector 4. Post - processing
- 7. 1. Colour normalisation • The zebrafish embryos are recorded in several sessions spanning a number of days for completing the whole dataset. The exposure time is not guaranteed to be the same for each session of recording through the light microscope, so the colour of each stack may be different. (Other factors: transparency of the specimen, light power…) [1] L. G. Nyu and J. K. Udupa, “On standardizing the MR image intensity scale,” Magnetic Resonance in Medicine, vol. 42, pp. 1072– 1081, 1999. We apply Image Intensity Standardisation (IIS), which was first introduced in [1] for intensity normalisation of 2D grey scale images.
- 8. 2. Colour Cell Region Detector Class – Imbalance Problem: In machine learning, to get better result, the number of P and N examples should be roughly equal. About 30 Positive example in one large stack. • We notice that all labelled cells have distinctive colours (colour features) from the background. • The binary Support Vector Machine (SVM) classifier based on RGB histogram features (SVM-RGB Histogram) is used as a rough and fast cell region detector. [2] M. Kolesnik and A. Fexa, “Multi-dimensional color histograms for segmentation of wounds in images,” in Image Analysis and Recognition. Springer, 2005, pp. 1014–1022. Original frame Cell Regions in blue
- 9. 3. Cell pixel detector- CNN Finding the cell regions is not enough, we need to find the precise location of the cell-center pixel. Labeled pixels Randomly pixels in the cell region: Negative pixels Adjacent pixels Rotate each patch & mirror Different degree Positive pixels Positive Patches Negative Patches Which feature could distinguish positive patches and negative patches? Such as edge, color histogram, Histogram of Oriented Gradients (HOG), Scale- Invariant Feature Transform (SIFT) or all of those (hand-crafted features) Over-sampling & Synthetic data Down-sampling
- 10. Convolutional Neural Network • Training patch size: 41*41 pixels based on the size of the neurons (123µm*123µm). • 0.5 million positive patches and 0.5 million negative patches. It takes a week to train the detector using MATLAB implementation in a PC with a Intel i5 CPU, 14GB memory and 64-bit operating system. • Max – pooling CNN with back propagation Using this CNN framework, the weight for each feature map is learned automatically.
- 11. How to process a stack (Detection process) 1. Detect Cell Region (using cell region detector) in each frame. 2. Get decision value map in the cell regions (using cell pixel detector for giving each pixel a decision value) for each frame. 3. Find 3D local maxima in the 3D smoothed decision value map 4. The 3D local maxima are cell center pixels
- 12. Result The detection result on two stacks. In the observer’s, the numbers of TH- labeled cells are 28 In the result processed by proposed method, there are 20 red, 11 blue and 6 green circles in the stack. Red, blue and green circles represent true positives, false positives and false negatives. Numbers in circles indicate the slice-location of each cell. Numbers of False Positives : N_FP Numbers of False Negative: N_PN Numbers of True Positives : N_TP Numbers of True Negative: N_TP Performance measures: Recall : R= N_TP/ (N_TP + N_PN) Precision: P= N_TP/ (N_TP + N_FP) F1 = 2*P*R/(P+R)
- 13. How to improve • Solve the class-imbalance problem using better method • Improve the CNN structure, Use 3D–RGB-CNN • Enlarge the dataset • Use GPU computing for accelerating the training and testing speed.
- 14. Thank you!
Editor's Notes
- Hi everyone, today I will give an 15 minutes talk about my previous work. (See title)
- First, I will introduce why we use zebrafish. Using zebrafish for disease research has many advantages, such as: 1,2,3,4,5,6 From one single cell to a embryo which can be used for disease research, it only takes 3 days to develop. We manly use this kind of fish for Parkinson’s disease research.
- We all know what is PD. The PD patients are hard to control their muscles. This is because, in their brain, the dopamine-producing nerve cells begin to die off, leaving too little of the chemical. Without dopamine, the cells that control movement cannot send messages to the muscles. However, until now, the causes of these dopaminergic neurons to waste away remain unknown. Neuroscientists use zebrafish for PD research, try to find what causes PD.
- When they use one kind of genetic zebrafish model, they found a phenomenon that: …
- This is the process how we producing zebrafish dataset. Firstly, we a specific process called whole-mount in situ hybridisation [haibridai’tion] (WISH) for tyrosine [‘tairesi:n] hydroxylase [hai’drosileis] (TH). Then the dopaminergic neurons will appear purple. We record the head of the zebrafish right side up through wide-field light microscope. By moving the focus plane from top to bottom, we will get a stack of images containing 3D information of the zebrafish. You can see from the image that the dopaminergic neurons are the purple ones, and we extract one cell from its clearest frame and it neighbor frames. The further away from the clearest frame, the more out-of-focus light there will be. We want to use supervised training techniques to capture this unique structure of the dopaminergic cell.
- This is the overall structure of our method, it mainly contains 4 steps: 1,2,3,4 I will talk about those four steps in detail.
- Firstly, it is the color normalization step. Why we should normalize the color of each stack? This is because when we recording the zebrafish images, different level of exposure time, transparency of the zebrafish embryos and the level of light power can cause different colors. We extend a existing method called IIS – image intensity standardization method, which is firstly introduced in 1999 for intensity normalization of 2D grey sale image by normalizing its histogram. Now we extend it for color normalization of 3D RGB images.
- Then it is the cell region detector. When we got the color normalized stack, there is a problem in machine leaning called class – imbalance problem. In order to get better performance, the number of positive and negative training examples should be roughly equal. The problem is we only have the positive labels which is the central pixel of each dopaminergic neuron. There is about 30 labeled pixels in one stack, so the rest of the pixels in the stack are negative examples. This step is more like a iteration of adaboost method to remove most of the negative examples which is not similar with the positive ones. This figure is the result. In this cell regions, we use a cell pixel detector to detect the precise cell position is the cell regions.
- In order to deal with the class-imbalance problem for training the CNN structure, we use over-sampling method and also produce synthetic data based on the original data. For each labeled pixel, we collect it neighbor pixels. For each positive pixel, we extract several patches by rotating and mirroring. For the negative samples, we randomly sampling the negative pixels in the cell region which is defined in last step. Then we make the number of positive and negative samples roughly equal.
- Then when we got the pre-trained detector,
- Finally, it is the result. We calculate the Recall, precision and F1, those three performance measures for validating our method.
- We improve our method in those four aspect. Then we get a latest result for one stack, and the red circle is the ground truth and the blue circle is the detection result. It is better than the result we show in the ISBI paper.