基于医学图像处理的的肺结节检测.zip

# 肺结节检测 ## Features - **3D** Segmentation & Classification with Keras - Fine **preprocessing** with scikit-image - Fine **visualization** for clarification - Modified UNet for **segmentation** - Modified VGG/Inception/ResNet/DenseNet for **classification ensemble** - Fine **hyperparameter** tunning with both models and training process. ## Code Hierarchy ``` - config.py # good practice to centralize hyper parameters - preprocess.py # Step 1, preprocess, store numpy/meta 'cache' at ./preprocess/ - train_segmentation.py # Step 2, segmentation with UNet Model - model_UNet.py # UNet model definition - train_classificaion.py # Step 3, classificaiton with VGG/Inception/ResNet/DenseNet - model_VGG.py # VGG model definition - model_Inception.py # Inception model definition - model_ResNet.py # ResNet model definition - model_DenseNet.py # DenseNet model definition - generators.py # generator for segmentation & classificaiton models - visual_utils.py # 3D visual tools - dataset/ # dataset, changed in config.py - preprocess/ # 'cache' preprocessed numpy/meta data, changed in config.py - train_ipynbs # training process notebooks ``` ## Preprocess - use `SimpleITK` to read CT files, process, and store into cache with numpy arrays - process with `scikit-image` lib, try lots of parameters for best cutting - binarized - clear-board - label - regions - closing - dilation - collect all meta information(seriesuid, shape, file_path, origin, spacing, coordinates, cover_ratio, etc.) and store in **ONE** cache file for fast training init. - see preprocessing in `/train_ipynbs/preprocess.ipynb` file Distribution of the lung part takes on a whole CT. <img src='./assets/preprocess-cover-ratio.png'> Tumor size distribution <img src='./assets/preprocess-diameter-mm.png'> ## Segmentation - A **simplified and full UNet** both tested. - **`dice_coef_loss`** as loss function. - Periodically evaluate model with **lots of metrics**, which helps a lot to understand the model. - 30% of negative sample, which has no tumor, for generalization. - Due to memory limitation, 16 batch size used. <img src='./assets/segmentation.png'> ## Classification ### VGG - A simplified and full VGG model both tested. Use simplified VGG as baseline. <img src='./assets/VGG.png'> Pictures tells that: **hyperparameter tunning really matters**. ### Inception - A simplified Inception-module based network, with each block has 4-5 different type of conv. - 1\*1\*1 **depth-size seperable conv** - 1\*1\*1 **depth-size seperable conv**, then 3\*3\*3 conv_bn_relu - 1\*1\*1 **depth-size seperable conv**, then 2 3\*3\*3 conv_bn_relu - AveragePooling3D, then 1\*1\*1 **depth-size seperable conv** - (optional in config) 1\*1\*1 **depth-size seperable conv**, and (5, 1, 1), (1, 5, 1), (1, 1, 5) **spatial separable convolution** - Concatenate above. <img src='./assets/Inception.png'> ### ResNet - use `bottleneck` block instead of `basic_block` for implementation. - A `bottleneck` **residual block** consists of: - (1, 1, 1) conv_bn_relu - (3, 3, 3) conv_bn_relu - (1, 1, 1) conv_bn_relu - (optional in config) kernel_size=(3, 3, 3), strides=(2, 2, 2) conv_bn_relu for compression. - **Add(not Concatenate)** with input - Leave `RESNET_BLOCKS` as config to tune <img src='./assets/ResNet.png'> ### DenseNet - `DenseNet` draws tons of experience from origin paper. [https://arxiv.org/abs/1608.06993](https://arxiv.org/abs/1608.06993) - 3 dense\_block with 5 bn\_relu\_conv layers according to paper. - transition\_block after every dense\_block, expcet the last one. - Optional config for **DenseNet-BC**(paper called it): **1\*1\*1 depth-size seperable conv**, and **transition_block compression**. <img src='./assets/DenseNet.png'> ## Fine Tunning & Experience Got - Learning rate: `3e-5` works well for UNet, `1e-4` works well for classification models. - Due to memory limitation, 16 batch size used. - Data Augumentation: shift, rotate, etc. - **Visualization cannot be more important!!!** - coord(x, y, z) accord to (width, height, depth), naughty bugs. - **Put all config in one file save tons of time. Make everything clean and tidy** - Disk read is bottle neck. Read from **SSD**. - Different runs has different running log dirs, for better TensorBoard visualization. Make it like **`/train_logs/<model-name>-run-<hour>-<minute>`**. - Lots of **debug options** in config file. - 4 times probability strengthened for tumors < 10mm, 3 for tumor > 10mm and < 30mm, keep for > 30mm. Give more focus on small tumors, like below. <img src='./assets/small-tumor.png'>