HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images

Related tags

Deep Learning deep-learning tensorflow keras segmentation image-segmentation attention-mechanism semantic-segmentation encoder-decoder-model focal-loss self-attention medical-image-segmentation histological-images dice-loss monuseg monuseg-challenge histology-images nuclie-segmentation glands-segmentation histoseg
Overview

HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images

Maintained - Yes Quick Attention Multi Loss Function Encoder-Decoder Network Semantic Segmentation Computational Pathology

Histological Image Segmentation
This repo contains the code to Test and Train the HistoSeg

HistoSeg is an Encoder-Decoder DCNN which utilizes the novel Quick Attention Modules and Multi Loss function to generate segmentation masks from histopathological images with greater accuracy.

Datasets used for trainig HistoSeg

MoNuSeg - Multi-organ nuclei segmentation from H&E stained histopathological images

link: https://monuseg.grand-challenge.org/

GlaS - Gland segmentation in histology images

link: https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/

Trained Weights are available in the repo to test the HistoSeg

For MoNuSeg Dataset link: https://github.com/saadwazir/HistoSeg/blob/main/HistoSeg_MoNuSeg_.h5

For GlaS Dataset link: https://github.com/saadwazir/HistoSeg/blob/main/HistoSeg_GlaS_.h5

Data Preprocessing for Training

After downloading the dataset you must generate patches of images and their corresponding masks (Ground Truth), & convert it into numpy arrays or you can use dataloaders directly inside the code. you can generate patches using Image_Patchyfy. Link : https://github.com/saadwazir/Image_Patchyfy

For example to train HistoSeg on MoNuSeg Dataset, the distribution of dataset after creating pathes

X_train 1470x256x256x3 
y_train 1470x256x256x1
X_val 686x256x256x3
y_Val 686x256x256x1

Data Preprocessing for Testing

You just need to resize the images and their corresponding masks (Ground Truth) into same size i.e all the samples must have same resolution, and then convert it into numpy arrays.

For example to test HistoSeg on MoNuSeg Dataset, the shapes of dataset after creating numpy arrays are

X_test 14x1000x1000x3 
y_test 14x1000x1000x1

Requirements

pip install matplotlib
pip install seaborn
pip install tqdm
pip install scikit-learn
conda install tensorflow==2.7
pip install keras==2.2.4

Training

To train HistoSeg use the following command

python HistoSeg_Train.py --train_images 'path' --train_masks 'path' --val_images 'path' --val_masks 'path' --width 256 --height 256 --epochs 100 --batch 16

Testing

To test HistoSeg use the following command

python HistoSeg_Test.py --images 'path' --masks 'path' --weights 'path' --width 1000 --height 1000

For example to test HistoSeg on MoNuSeg Dataset with trained weights, use the following command
python HistoSeg_Test.py --images 'X_test_MoNuSeg_14x1000x1000.npy' --masks 'y_test_MoNuSeg_14x1000x1000.npy' --weights 'HistoSeg_MoNuSeg_.h5' --width 1000 --height 1000
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Comments
  • Dice's calculation is obviously wrong

    Dice's calculation is obviously wrong

    In the GlaS dataset, your IoU is only 76.73. According to the mathematical definition of IoU and Dice coefficient, your Dice is absolutely impossible to reach 99.09, so your Dice coefficient calculation is obviously wrong. You should check your code implementation of Dice coefficient calculation.

    在GlaS数据集里，你的IoU只有76.73，根据IoU和Dice系数的数学定义，你的Dice是绝对不可能达到99.09的，所以你的Dice系数计算显然是错误的。你应该检查一下你关于Dice系数计算这部分的代码实现。

    opened by Frank-Star-fn 2
  • About F1-score calculation

    About F1-score calculation

    I want to ask a question.

    When you were calculating F1, did you use the algorithm provided by the GlaS competition official website to calculate each object? That is, is your calculation the same as instance segmentation?

    Or, is your calculation in f1 score is calculated by pixel?

    Thank you.

    opened by aihcyllop 0
  • Sorry I don't know why my training .npy file does not work, could you please tell me why? or provide .npy file for train?

    Sorry I don't know why my training .npy file does not work, could you please tell me why? or provide .npy file for train?

    Sorry I don't know why my training .npy file didn't work, could you please tell me why? Or provide .npy file for train?

    The following are the errors that result from training with .npy file：

    Traceback (most recent call last): File "e:\HistoSeg\HistoSeg-Tensorflow\HistoSeg_Train.py", line 629, in results=model.fit(X_train, y_train, batch_size=batch_arg, epochs=epochs_arg, callbacks=callbacks, validation_data=(X_test, y_test) , verbose = 1)
    File "E:\Users\15199\anaconda3\envs\Histoseg\lib\site-packages\tensorflow\python\keras\engine\training_v1.py", line 793, in fit return func.fit( File "E:\Users\15199\anaconda3\envs\Histoseg\lib\site-packages\tensorflow\python\keras\engine\training_arrays_v1.py", line 644, in fit return fit_loop( File "E:\Users\15199\anaconda3\envs\Histoseg\lib\site-packages\tensorflow\python\keras\engine\training_arrays_v1.py", line 380, in model_iteration
    batch_outs = f(ins_batch) File "E:\Users\15199\anaconda3\envs\Histoseg\lib\site-packages\tensorflow\python\keras\backend.py", line 4067, in call fetched = self._callable_fn(*array_vals, File "E:\Users\15199\anaconda3\envs\Histoseg\lib\site-packages\tensorflow\python\client\session.py", line 1483, in call ret = tf_session.TF_SessionRunCallable(self._session._session, tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found. (0) INVALID_ARGUMENT: assertion failed: [labels out of bound] [Condition x < y did not hold element-wise:] [x (metrics/mean_io_u/confusion_matrix/control_dependency:0) = ] [0 0 0...] [y (metrics/mean_io_u/confusion_matrix/Cast_2:0) = ] [2] [[{{function_node metrics_mean_io_u_confusion_matrix_assert_less_Assert_AssertGuard_false_7653}}{{node Assert}}]] [[expanded_conv_11_project_BN/cond/then/_980/FusedBatchNormV3/_6151]] (1) INVALID_ARGUMENT: assertion failed: [labels out of bound] [Condition x < y did not hold element-wise:] [x (metrics/mean_io_u/confusion_matrix/control_dependency:0) = ] [0 0 0...] [y (metrics/mean_io_u/confusion_matrix/Cast_2:0) = ] [2] [[{{function_node metrics_mean_io_u_confusion_matrix_assert_less_Assert_AssertGuard_false_7653}}{{node Assert}}]]

    Thanks.

    opened by DanggoRyo 2
Owner
Saad Wazir
Saad Wazir is currently working as a Researcher at Embedded Systems & Pervasive Computing (EPIC) Lab in National University of Computer and Emerging Sciences (F
Saad Wazir
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