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https://github.com/fitushar/densevnet3d_chest_abdomen_pelvis_segmentation_tf2
This Repo containes the implemnetation of DenseVent in tensorflow 2.0 for chest-abdomen-pelvis (CAP) Segmentation
https://github.com/fitushar/densevnet3d_chest_abdomen_pelvis_segmentation_tf2
3d-segmentation densevnet densevnet3d multi-class-dice multi-class-dice-tf2 multi-class-segmenation tensorflow2-3d-segmentation-model tensorflow2-models tfdata tfrecords-training
Last synced: about 1 month ago
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This Repo containes the implemnetation of DenseVent in tensorflow 2.0 for chest-abdomen-pelvis (CAP) Segmentation
- Host: GitHub
- URL: https://github.com/fitushar/densevnet3d_chest_abdomen_pelvis_segmentation_tf2
- Owner: fitushar
- Created: 2020-04-24T01:22:32.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-04-24T02:42:26.000Z (over 4 years ago)
- Last Synced: 2024-08-03T06:01:14.153Z (5 months ago)
- Topics: 3d-segmentation, densevnet, densevnet3d, multi-class-dice, multi-class-dice-tf2, multi-class-segmenation, tensorflow2-3d-segmentation-model, tensorflow2-models, tfdata, tfrecords-training
- Language: Python
- Size: 410 KB
- Stars: 2
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# DenseVNet3D_Chest_Abdomen_Pelvis_Segmentation_tf2
This Repo containes the implemnetation of DenseVent in tensorflow 2.0 for chest-abdomen-pelvis (CAP) Segmentation
## Description:
This is a implemnation of the DenseVnet in tensorflow 2.0. DesnVnet(Gibson et al.,"Automatic multi-organ segmentation on abdominal CT with dense V-networks" 2018.) a 3D state-of-art segmentation model for chest-abdomen-pelvis (CAP) Segmentation.
```
Input
|
--[ DFS ]-----------------------[ Conv ]------------[ Conv ]------[+]-->
| | | |
-----[ DFS ]---------------[ Conv ]------ | |
| | |
-----[ DFS ]-------[ Conv ]--------- |
[ Prior ]---
```
Reference Implementation:
* a)https://github.com/baibaidj/vision4med/blob/5c23f57c2836bfabd7bd95a024a0a0b776b181b5/nets/DenseVnet.py
* b)https://niftynet.readthedocs.io/en/dev/_modules/niftynet/network/dense_vnet.html#DenseVNet
## Files:
* i) `DenseVnet_config.py -|--> All the Netword and Training configuration`
* ii) `DenseVnet_Loss |--> Losses and Matrics function. Binary And Multi-class Dice Coefficent and Dice Loss`
* iii) `DenseVnet3D |--> Network architecture`
* iv) `Train_DenseVnet3D |--> Training Script. it has tfrecord decoder, tfdataset reading pipeline and training loop.`
## How to run
To run the model all is to need to configure the `DenseVnet_config.py` based on your requiremnet.
```ruby
###---Number-of-GPU
NUM_OF_GPU=2
DISTRIIBUTED_STRATEGY_GPUS=["gpu:0","gpu:1"]
###----Resume-Training
'''
if want to resume training from the weights Set
RESUME_TRAINING=1
'''
RESUME_TRAINING=0
RESUME_TRAIING_MODEL='/Path/of/the/model/weight/Model.h5'
TRAINING_INITIAL_EPOCH=0
#####-----Configure DenseVnet3D---##########
SEG_NUMBER_OF_CLASSES=31
SEG_INPUT_PATCH_SIZE=(128,160,160, 1)
NUM_DENSEBLOCK_EACH_RESOLUTION=(4, 8, 16)
NUM_OF_FILTER_EACH_RESOLUTION=(12,24,24)
DILATION_RATE=(5, 10, 10)
DROPOUT_RATE=0.25
##Training Hyper-Parameter
TRAIN_CLASSIFY_LEARNING_RATE =1e-4
SEG_LOSS=Avg_Dice_loss
OPTIMIZER=tf.keras.optimizers.Adam(lr=TRAIN_CLASSIFY_LEARNING_RATE,epsilon=1e-5)
SEG_METRICS=Avg_Dice_matrix
BATCH_SIZE=2
TRAINING_STEP_PER_EPOCH=math.ceil((76)/BATCH_SIZE)
VALIDATION_STEP=math.ceil((6)/BATCH_SIZE)
TRAING_EPOCH=10000
NUMBER_OF_PARALLEL_CALL=2
PARSHING=2*BATCH_SIZE
#--Callbacks-----
ModelCheckpoint_MOTITOR='Model_31_SEG_DenseVnet_April16_2020'
TRAINING_SAVE_MODEL_PATH=''/Path/to/save/model/weight/Model.h5''
TRAINING_CSV='Log_31_SEG_DenseVnet.csv'
LOG_FILE_NAME="Log_31_SEG_DenseVnet." #|lOG FOLDER NAME
SAVE_MODEL_NAME="Org31SEG_DenseVnet_{val_loss:.2f}_{epoch}.h5" #|Model name
#tfrecords--paths
TRAINING_TF_RECORDS='/Training/tfrecords/path/'
VALIDATION_TF_RECORDS='/Val/tfrecords/path/'
```
## Dice Loss
Loss function and matrix are very import. as tensorflow doesn't have a build in multi-class segmentation dice in this project I gave implemened Average Dice-Loss and Dice Matrix. in `DenseVnet_Loss.py` contains the loss and matrix functions.
```ruby
#|Dice Coefficient for binary Segmentation task
def dice_calculator(mask_true, mask_pred):
num_sum = 2.0 * tf.keras.backend.sum(mask_true * mask_pred) + tf.keras.backend.epsilon()
den_sum = tf.keras.backend.sum(tf.keras.backend.square(mask_true)) + tf.keras.backend.sum(tf.keras.backend.square(mask_pred))+ tf.keras.backend.epsilon()
dise=(num_sum/den_sum)
return dise
#|Average Dice Loss for multiclass-Segmenatiom task
def Avg_Dice_loss(y_true, y_predicted,num_classes=31):
clas_dice_list=[]
for i in range(0,num_classes):
mask_true = tf.keras.backend.flatten(y_true[:, :, :, :, i])#
mask_pred = tf.keras.backend.flatten(y_predicted[:, :, :, :, i])#
class_dice=dice_calculator(mask_true,mask_pred)
clas_dice_list.append(class_dice)
avg_dice=tf.math.reduce_mean(clas_dice_list,axis=0)
dice_loss=1-avg_dice
return dice_loss
#|Average Dice matricx for multiclass-Segmenatiom task
def Avg_Dice_matrix(y_true, y_predicted,num_classes=31):
clas_dice_list=[]
for i in range(0,num_classes):
mask_true = tf.keras.backend.flatten(y_true[:, :, :, :, i])#
mask_pred = tf.keras.backend.flatten(y_predicted[:, :, :, :, i])#
class_dice=dice_calculator(mask_true,mask_pred)
clas_dice_list.append(class_dice)
avg_dice=tf.math.reduce_mean(clas_dice_list,axis=0)
dice_score=tf.print(clas_dice_list[2:9],summarize=10)
return avg_dice
```
## Sample Segmentation
