Restructure + Add Gradio Interface

README.md

@@ -1,40 +1,44 @@
-# Deep-Learning-liver-segmentation-project
- <em>Final project of the Deep Learning course followed at <a href='https://www.imt-atlantique.fr/en'>IMT Atlantique</a>.</em></br></br>
-<p>The code of this project is largely inspired by that of <a href='https://github.com/jocicmarko/ultrasound-nerve-segmentation'> this repository</a>, a tutorial for a kaggle competition about ultrasound image nerve segmentation. The goal of this project is to adapt the code to the segmentation of liver images as described in this article https://arxiv.org/pdf/1702.05970.pdf.
- </p>
+# Liver Segmentation with U-Net
+
+*Final project of the Deep Learning course followed at [IMT Atlantique](https://www.imt-atlantique.fr/en).*
+
+The code of this project is largely inspired by [this repository](https://github.com/jocicmarko/ultrasound-nerve-segmentation), a tutorial for a Kaggle competition about ultrasound image nerve segmentation. The goal of this project is to adapt the code to the segmentation of liver images as described in [this article](https://arxiv.org/pdf/1702.05970.pdf).
 
 ## Data
-The data to be used are available in NifTi format <a href='https://www.dropbox.com/s/hx3dehfixjdifvu/ELU-502-ircad-dataset.zip?dl=0'>here</a>. 
-This dataset consists of 20 medical examinations in 3D, we have the source image as well as a mask of segmentation of the liver for each of these examinations. We will use the nibabel library (http://nipy.org/nibabel/) to read associated images and masks.
 
-## Model
-<p>We will train a U-net architecture, a fully convolutional network. The principle of this architecture is to add to a usual contracting network, layers with upsampling operators instead of pooling. This allow the network to learn context (contracting path), then localization (expansive path). Context information is propagated to higher resolution layers thanks to skip-connexions. So we have images of the same size as input</p>
+The data to be used are available in NifTi format [here](https://www.dropbox.com/s/hx3dehfixjdifvu/ELU-502-ircad-dataset.zip?dl=0).
 
+This dataset consists of 20 medical examinations in 3D, we have the source image as well as a mask of segmentation of the liver for each of these examinations. We will use the [nibabel library](http://nipy.org/nibabel/) to read associated images and masks.
 
-<p align="center"><img src="img/u-net-architecture.png" style></img></p>
+## Model
+
+We will train a U-net architecture, a fully convolutional network. The principle of this architecture is to add to a usual contracting network, layers with upsampling operators instead of pooling. This allows the network to learn context (contracting path), then localization (expansive path). Context information is propagated to higher resolution layers thanks to skip-connections. So we have images of the same size as input.
 
+![U-Net Architecture](img/u-net-architecture.png)
 
-<p>in the data.py script, we perform axial cuts of our 3D images. So 256x256 images are input to the network</p>
+In the data.py script, we perform axial cuts of our 3D images. So 256x256 images are input to the network.
 
 ## Evaluation
 
-As metric we will use the <a href='https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient'>Dice coefficient</a> (which is quite similar to the Jaccard coefficient)
+As metric we will use the [Dice coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) (which is quite similar to the Jaccard coefficient).
 
 ## How it works
-<ol><li>First download the data whose link has been given previously</li>
-<li>Create a 'raw' folder
-<li>In the 'raw' folder, create a 'test' folder, and a 'train' folder
-<li>Then separate the data in two sets (train and test, typically we use 13 samples for the train set and 7 for the test set) and put them in the corresponding directories that you can find in the 'raw' folder</li>
-<li>Run data.py , this will save the train and test data in npy format</li>
-<li>Finally launch the notebook, you can observe a curve of the Dice coef according to the number of epochs and visualize your predictions in the folder 'preds'</li>
- </ol>
- (Feel free to play with the parameters : learning rate, optimizer etc.)
- 
- ## Some results
- 
- 
-<p>Finally we get this kind of predictions for a particular cut (thanks to the mark_boundaries function that you can find in the notebook), we can observe the liver is delimited in yellow</p>
-<p align="center"><img src="img/segmentation-example1.png"></img></p>
-
-<p>The evolution of the Dice coef for 20 epochs, this plot shows that we have consistent results and a test Dice coef reaching almost 0.87</p>
-<p align="center"><img src="img/dice-20epochs-example.png"></img></p>
+
+1. First download the data whose link has been given previously
+2. Create a 'raw' folder
+3. In the 'raw' folder, create a 'test' folder, and a 'train' folder
+4. Then separate the data in two sets (train and test, typically we use 13 samples for the train set and 7 for the test set) and put them in the corresponding directories that you can find in the 'raw' folder
+5. Run data.py, this will save the train and test data in npy format
+6. Finally launch the notebook, you can observe a curve of the Dice coef according to the number of epochs and visualize your predictions in the folder 'preds'
+
+(Feel free to play with the parameters: learning rate, optimizer etc.)
+
+## Some results
+
+Finally we get this kind of predictions for a particular cut (thanks to the mark_boundaries function that you can find in the notebook), we can observe the liver is delimited in yellow:
+
+![Segmentation Example](img/segmentation-example1.png)
+
+The evolution of the Dice coef for 20 epochs, this plot shows that we have consistent results and a test Dice coef reaching almost 0.87:
+
+![Dice Coefficient Evolution](img/dice-20epochs-example.png)

app.py

@@ -0,0 +1,27 @@
+import gradio as gr
+from src.model.model import get_unet
+# from src.dicom_handler.dicom_predictor import predict_dicom
+from src.nifti_handler.nifti_predictor import predict_nifti
+
+# Load the model and weights
+model = get_unet()
+model.load_weights('models/weights.h5')
+
+# Define mean and std (should have saved these during training)
+mean = 0  # Replace with actual mean
+std = 1   # Replace with actual std
+
+def predict_liver_segmentation(file):
+    # segmented = predict_dicom(file.name, 'models/weights.h5')
+    segmented = predict_nifti(file.name, 'models/weights.h5')
+    return segmented
+
+iface = gr.Interface(
+    fn=predict_liver_segmentation,
+    inputs=gr.File(label="Upload NIfTI file (.nii.gz)"),
+    outputs=gr.Image(label="Segmentation Result"),
+    title="Liver Segmentation from NIfTI",
+    description="Upload a liver CT scan NIfTI file (.nii.gz) to get the segmentation result."
+)
+
+iface.launch(share=True)

train.ipynb → notebooks/train.ipynb

@@ -2,25 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n",
-      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\dicom\\__init__.py:53: UserWarning: \n",
-      "This code is using an older version of pydicom, which is no longer \n",
-      "maintained as of Jan 2017.  You can access the new pydicom features and API \n",
-      "by installing `pydicom` from PyPI.\n",
-      "See 'Transitioning to pydicom 1.x' section at pydicom.readthedocs.org \n",
-      "for more information.\n",
-      "\n",
-      "  warnings.warn(msg)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "from skimage.transform import resize\n",
@@ -338,7 +322,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -378,7 +362,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,