diff --git a/README.md b/README.md index b87bb481fc02a9070118df8b6f15bae981f93339..e241ef95f81865157204719e26e94cb836a7bea8 100755 --- a/README.md +++ b/README.md @@ -1,40 +1,44 @@ -# Deep-Learning-liver-segmentation-project - Final project of the Deep Learning course followed at IMT Atlantique.

-

The code of this project is largely inspired by that of this repository, 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. -

+# 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 here. -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 -

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

+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. -

+## 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) -

in the data.py script, we perform axial cuts of our 3D images. So 256x256 images are input to the network

+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 Dice coefficient (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 -
  1. First download the data whose link has been given previously
    2. -
  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. -
  5. Run data.py , this will save the train and test data in npy format
    6. -
  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'
    7. -
- (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

-

- -

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

-

+ +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) \ No newline at end of file diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..222ea225c7b47345f61523035d1b9f71448f69d4 --- /dev/null +++ b/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) \ No newline at end of file diff --git a/train.ipynb b/notebooks/train.ipynb similarity index 98% rename from train.ipynb rename to notebooks/train.ipynb index da20c9df07a6822d10d34c7280085ceb78fd56db..d00c5df590f4a3222f843c5dae9171f9dee0fe42 100755 --- a/train.ipynb +++ b/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": { - "image/png": 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1DY9dNZxTspo/TEbR/iqm/2cp320pYsYZfbl9fL+glJQ1KbSAlhg6G2DOnDmce+65dO7c2W+x+ltJRTWvLsnlhUXb2LJnP6mJsfzsjCyuPKE7ndsHrgisVCAMy+zAm7eczI3PLeG6ZxZzzwWDuHp0jyN+nw15pdzw3BJ2l1Tw0BXDmDSsqx+i9Y0mhRbgy9DZvpgzZw7Dhw9vlUlh/e5Snl+4lTe+30F5VS3Du3fg9iuGMXFwl7Do8KPCV7eO7Zg3/SRmvPQ9v3tzFVv27Oc35w7wuST8RXYBt764jNjoSOZOG83w7oHvB+FKk4KfPffcczz66KNUVVVx8sknM3v2bOrq6pg6dSrLly/HGMO0adPIyMhg+fLlXH755cTHxx9RCSNYamrr+GhNHs8t3MqizUXEREUwaegxXHNST4Z004F5VPhIiovmyWtG8sd31/L011vYVrifh6443usQ3vWe+2Yr97y9mn4ZSTx93Si6dgj+WEttLym8PxN2/9Cy79l5CEy8/4hftmrVKt544w2++eYboqKimDZtGnPnzqVPnz7s2bOHH36wce7bt48OHTrwyCOPMHv2bIYNG9ay8bewPWWVvLw4h/8s2sau4gq6dohn5sRjuWxkps5mpcJWVGQEd18wiN5pCdw9fzWXPL6Qp68dyTEeDvTVtXXc+/YaXli0jfED0vnnFceHTHPr0Iiijfr4449ZvHgxI0fanuUHDhwgMzOTc845h/Xr13Pbbbdx7rnncvbZZwc50qYZY1iRW8zz32zlnZW7qKqt45SsVO6dNJgzjk3Xi8ZKOa45qSfdO7Xj1v9+z4WP/o+nrh3Jcd06NKwvLq/mlv8u4+uNe/jJqb351YRjQ+r30/aSQjPO6P3FGMP111/PH/7wh8PWrVy5kvfff5+HH36Y1157jSeeeCIIETZuf2UN/9u4h8+zC/hifQE79h0gISaSKSdk8uOTetI3PTHYISoVksb1T+e1m0/m+mcXc9m/F/LPy4cxYXAXtuzZzw3PLSanqJy/TD6Oy0ZlBjvUw7S9pBBCxo8fzyWXXMJtt91GamoqhYWF7N+/n/j4eOLi4rj00kvp1asX06dPByApKYnS0tKgxWuMYUN+GZ+vz+fz9QUs3lpEda0hISaSk/umcusZfTn/uC4khdEwwko1V//OSbx5yxhuen4J0/+zjGtP6sGby3cSIfDCDScyundKsEP0SJOCHw0ZMoTf//73jB8/nrq6OqKjo3n88ceJjIzkhhtuwBiDiPDAAw8AMHXqVG688caAXmguqy8NrC/gy2xbGgDol5HI1DG9GNcvjZE9O2kLIqWaIS0plrnTRnPnvBU8t3AbfdMTefrakfRISWj6xUGiQ2e3Ys35u40xZOcdLA0s2WZLA4mxUYzpm8K4/umc1i/N48UxpVTz1NUZvsguYETPjkGbsEeHzlYNjDEs2lzE/BU7+WJ9PjuL7RSgx3ZO4vqxvRjXL50RPTpqaUApP4mIsCOttgaaFNqwvJIKXl2ayytLcthWWE5ibBRj+6Yy48w0TuufFpbzzyqlGtdmkkJ9/Xy48FbtV11bx2fr8nllSQ6frS+gts5wYq9O3D4+i4mDu+gMY6p5qg/A7lXQZShEtcK+KDVVULoTOvSw868qr9pEUoiLi6OwsJCUlJSwSAzGGAoLC4mLOziW0JY9+3llSQ6vLs2loLSStKRYpp3am8tGZtIrNXQvaqkQVlcH2/4HK+fCmvlQWQKdesNZf4BjzwvNg6sxULID8tZA3irIWw35a2BPNtTVwOSnYcglwY4ypLWJpNCtWzdyc3MpKCgIdigBExcXR2pGF15flsvLi3P4dksRkRHC6f3TuHxUd07vn0aUzkKmmiN/nU0EK+dBSS7EJMKAC6D7aFj4KLx8FfQYC+f8CY4JYu/7ylLIX+sc/Nc4CWA1VBQf3KZ9JmQMgn4TYOXLsGKuJoUmtImkEB0dTa9evYIdRsCs2lHM04u389byLyitqKFHSjt+eU5/LhnRLaCTcSgfGQPL/wsLZ0P7btBlmD2YdhkGyceExhl3aR6setUeOHetAImEPmfAWfdA/3MhxpksfthVsOxZ+Ow+eGIcDJ0CZ/6f/Tv8qaocNn1iY6svBezbdnB9TBJkDITBkyF9IGQMhvQBEH+wJzGmzv4P9hdCQmj2EQgFbaJJajg4UFXLvKU5vLw4h9U7S4iNiuDcIV24bGQmJ/bqpDOUhaqyAnj7Nlj/rh1Dq64WCtbZAxRAQppNDl2GHkwU7bsFJlFU7Yd179qz582f2Zi6DIOhV9iDa2IjrWUqiuGrv8Gif9kEMmYGnDwDYluwl7sxkPMdLH8RVr9hq68kAlKybALIGATpg+x9h+5Nf2a7VsK/T4Hz/wkjp7ZcnK2Er01SNSm0Ah+tyePu+avZse8AA7skc8UJmUwa2lVnKgt1696F+TNsNceZs2D0TyEiwp715q2Cncth13J7X7AOTK19XbsUl9LEUPvYl4OeL+pqYcsXsOJlWPs2VO+3VSzHXQbHXQ5p/Y/s/fZuhY/vgdWvQ2JnW2oYOgUijqJBQ8lOWPGSLV0VboTodjDwQpusMk+E6GaWho2B2aMgqTNc907z42ulNCm0ATlF5dzz9ho+XptHv4xE7rlgMCf10WJvyKsohg/usme4nY+Di5+wVRmNqT5g68R3fu8kihVQsNZeHAWI72TPiGMS7Jl5hHOTSIiIch5HOMujPGwTaZPTmvlQthti28OgSXDcFdD9JJusjkbOd/Zv3rHElojO/hP0Ps3311dXwLp3bCKoL7X0GAPDroSBkyD26Ke6BOCzP8MXD8Av1tnkEEZCIimIyATgISASeMoYc7/b+h7AHCANKAKuNsbkNvae4ZAUqmrqePKrzTzy6QYiRLh9fBZTx/QiWi8ch74tX8KbP7UtYE75BZz6q+Y34ayusIlil1OiyF8LNZX2gFlXY8/6Ta29d31sap31dS7ra2xi6Dvelgj6TWj+Gbc3xsCq12zJoXi7vRZx1r2QmuV9+x1LbfJc9ZpNpu0zbUlj2BTb0qmlFayHR0+ACQ/A6Okt//4hLOhJQUQigWzgLCAXWAxMMcascdlmHvCOMeY5ETkDmGqM+XFj79vWk8I3m/bwf2+uYlPBfiYM6sysHw3UISdag+oD8MkfYNGj0KkPXPRvyBwV7KiCo7oCvv0XfPk3qDkAI2+AcTOhXSe7vnS3vY6x/L+wZz1ExcPAC2ypoOepR19qacq/xkJ0PNz4kX/3E2JCYZiLE4CNxpjNTkBzgUnAGpdtBgI/dx5/Brzpx3hCWn5pBfe9u5Y3l++ke6d2PHPdqFbTLT7s7fwe3phurwuMusm22IkJ474h0XEw9ucw7Gr4/D5Y/KRt4jrqJti9EjZ+bEs7maPhRw/DoIsgLjlw8Q2+GD65B/Zug45HPp9yW+fPpNAVyHF5nguc6LbNCmAytorpIiBJRFKMMYWuG4nINGAaQPfu3f0WcDDU1hle/HYbDy5YT2V1HTPOzOKn4/poz+PWoLYGvv67raNOSIerX4e+ZwY7qtCRmAbn/wNOmAYf/h989VdI7moTxtArIbVvcOKqTwqr34CxtwcnhhDmz6TgqamEe13VncBsEbkO+BLYAdQc9iJjngCeAFt91LJhBs+KnH389s0fWLWjhLF9U7l30iB6p+nENa3Cng3wxk9snfiQS+HcByE+uBOuh6z0AXD1q1CcC0ldjq5lUkvo2BO6jrTXMTQpHMafSSEXcJ1WqBuw03UDY8xO4GIAEUkEJhtjimnjisurefDDdbz47XbSEmOZfeXxnDekS1gM0dHq1dXB4qfgo1m2muSSZ+yZp2pa+27BjuCgwZNhwV02uXu7EB6m/JkUFgNZItILWwK4ArjSdQMRSQWKjDF1wF3YlkhtljGG15ft4L731rK3vIqpJ/fi52dl6UxmgWCMHcenusK2BoqMdbl3bvXLouIgMubwfgHFufDWLbD5c+h7FlzwCCR3Ccqfo47SoAthwW9g1esw7tfBjiak+C0pGGNqRORWYAG2SeocY8xqEbkXWGKMmQ+MA/4sIgZbfXSLv+IJtk0FZdz1+g98t6WI4d078PwNJzDomPbBDit8LJwNH/7uyF4T6ZY0Duy1y8//B4yYGhrDU6jmST7G9oNY9Sqc9iv9X7rw69hHxpj3gPfcls1yefwq8Ko/Ywi2ujrDs99s5YEP1hEfE8n9Fw/hspGZOixFIJUVwBd/sWP5jLsLairsUMq1lbbdf22VD8uqIDIaTv4ZpPQJ9l+kWsLgi+HdO2xfkM6Dgx1NyGgTA+KFqty95fxy3koWbi7kzGPT+fPkIaQn6YB1AffpH6C6HCb+ReuP1UEDJ8F7v7QXnDUpNNCk4AfGGOYtyeXed9ZgjOEvk4/j0pHd9EJyMOxaCcueh9E3a0JQh0pIhd7jbFI4c5ZWITl03IQWll9awY3PLeFXr61kcNdkPrj9VC4blakJIRiMsePxxHe09cZKuRs82Q7BvWNZsCMJGVpSaEHvrtzF7978gfKqWmadP5DrTu6p1w6Cae182PY1nPc37UOgPDv2PHgnxpYWuo0IdjQhQUsKLWBfeRUzXvqeW/67jO6d2vHujFO4fmwvTQjBVF1hWxulD4Th1wU7GhWq4jvY5sWrX7d9UJSWFI7WZ+vzmfnaSgrLqvjFWf24eVwfnQYzFCx6FPZth2vegkj9mqtGDL7YToK0fSH0HBPsaIJOfy3NtL+yhj++u5aXvttOv4xEnr52FIO7ar+DkFCyy47Q2f88eyFRqcb0n2gn8ln1miYFtPqoWb7bUsSEh75k7uLt/OTU3sy/dawmhFDyyb22n8HZfwh2JKo1iEmw80usedMOcugP1RV2gp/CTf55/xakSeEIVFTX8qd313D5EwsRhFd+chJ3nTtARzQNJTuWwor/2iao2slM+WrwZCgvtFOV+sMX99vb3KvsdKwhTJOCj3bsO8CPHvmaJ7/awpUndOf9205hVM9OwQ5LuapvgpqQBqf+MtjRqNak73iITbZjIbW0HUvhfw/Z+SMK1sH7of3d1KTgo5cX57CpoIxnp47iTxcNISFWL8eEnFWvQc63cMb/BXbSFtX6RcfBsefD2rftMCctpbrCTs+a1AWuegVOuQO+/w+seLnl9tHCNCn4aENeKT1SEhjXPwxnQ9u3HV7/CTyYBbtXBTsaz6rK7XDWnYfA8VcHOxrVGg2eDJXFdma4lvLFA7Z08KOHIa49jPsNdD8Z3vk5FGS33H5akCYFH63PK6VfRphNgFNeBAt+C4+MtLNU1VbBq1Ohan+wIzvcNw9DyQ47IXuwJ3FRrVPv0yC+ky1xtoQdS+F//7QnKVnj7bLIKJj8lC2ZzLvOzu0dYjQp+KCyppZtheX0y0gKdiiBUV1h60AfHgYLH4Uhl8CMZXD5C3ZSkvdCrE60OBe+/icMvFCbFKrmi4y2g+Stf//oT3xqKg9WG51z36Hr2neFi56A/NXwwcyj248faFLwweaC/dTWGbLaelKoq4XlL8EjI2xVTOaJcPP/4MLH7KxZvU61YwgtfxFWzA12tAd9fLedCP6se4MdiWrtBk+2I+pmf3B07/P5/U610UO22shd1ngYczssfRZ+CK3ZAzQp+CA7rxSg7VYfGQMbPoZ/nwpvTrcTrl/7Nlw1DzIGHbrtab+GHmPhnTtsqSHYcr6DH+bZeQ469gh2NKq163EyJHY+ulZIh1QbneV9uzN+Z0+83r4tpPovaFLwwYa8MiIjhF6pCcEOpeXt/B6enwQvToaqMrhkDtz4qS0VeBIRCZOfdKkTrQhouIeoq4P3f21/xGN/Hrw4VNsREQmDLoINH0JFM6aLr6mEN2+x38mz/9T4tpHR9vcWGQ3zrg3ub8mFJgUfrM8rpVdqArFRbegC5t6t8OoN8MQ4yFtlL9DestgWnyOa+FokHwMXPm5f9+FvAxGtZytfhp3LYPzdENtGS3Eq8AZPto0q1r175K/94gEoWAsXPGwH22tK+272t7T7h+D+llxoUvDBhrbU8mh/oe3g9chI+6U/5U6YsRxGT7eT1vuq39m2ymbxU7D6Tf/F601lmb2W0HUEHHd54Pev2q5uI6F99yNvhbRjmW3wMKyJaiPYfofmAAAbT0lEQVR3/SfASbcG77fkRpNCEyqqa9lWVE5Weiu/yFxZCl/9zbYo+vZxGHYlzPgezjyKjl5nzIKuI2H+z2zJI5C+/geU7YYJ9zddslHqSIjYkVM3fWZPonxR39ooMQPOaaLayJPxdx/8LRVtPvLXtyD9NTVhY34ZxtB6m6MWbYEPfgN/H2gHius5Fm5eaIu3yV2O7r2jYmydKAKvXm8ntw+Evdvgm0dgyKWQeUJg9qnCy+DJYGph7Vu+bV9fbfSjh3yrNnIXGQ2XPmMT0rzrWrZX9RHSpNCE+pZH/Tu3ouojY2Dz5/DSFHj4ePju35B1Ntz4CUx5CdKPbbl9dewBkx6xLS4+uafl3rcxH80CibBnV0r5Q+chkJLlWyukhmqjq2y1anN16A4X/gt2rYAP/6/573OUdACfJmTnlREdKfRIaQUtj6rK7cXXb/9tz1rapcKpd8LIG46+VNCYgZNg1I2wcLZttdTvHP/ta+v/7BDH4+6yF+mU8gcR22nz8/vt/Bzefj8N1Ubph3dSa45jz4PRP4VFj9lS/cALjv49j5CWFJqwIa+U3qmJRIfybGr7cuzZ898HwDu32670kx6Dn6+2baH9mRDqnf0nyBgCb0yH4h3+2Uddre0BmtwNTp7hn30oVW/QxYCxJyHefPEXp9rIx9ZGvhh/DxwzHN66NfDX6tCk0KTs/FKyQrHlkTGw7Rt4+cfw0HG2jr33aTD1ffjJV3D8VbYvQaBEx9k60ZpKeO1G/0xWsvxF2L0SzroHYtq1/Psr5Sqtn61G8tYKaccy2+DhaKuN3EXF2N8SwLypgbtW59Ck0Ijyqhpyig6E1kXm6gr4/kXb+/iZibDlS3vWfNtKuOx52yNTJDixpWbB+X+H7d/YC28tqXiHvVCeeaK9CKhUIAyeDLmLDz9jr6mEt25puWojdx17wqTZth/Ox3e3/Ps3QpNCIzbklQEh0vKoshQ+/SP8YxC89VOorbYtHe5Ya8+cO2QGO0Jr6BX2zOnLB2FzC8xitfN7eO0mWxqqKLZNUIOV9FT4GXSxvV/9xqHLv3wQ8tc0v7WRLwZeACdMg0WPwrr3/LMPDzQpNCJkxjyqqbItib78q22Cec18+OlCGHFdaFajnPugLTW8fhOU5R/56+tqYc18mDPR9rhe/x6Muglu+Ra6Dm/xcJXyqmMP6Dbq0Cqknd/DV3+HoVf6t1EFwNl/hC5D4c2b7bwmAaBJoREb8suIiYoIbssjY2yHlq1fwUWP2yalvU8L7bPlmAS45Bl7Zv/GT+wYRb6oKLFDdT88DF75MZTk2qL5HWtg4v3Qqbd/41bKk8GT7TAUBdn2BK2+tdEEP1QbuYuKtb+lulrbF6i22u+71KTQiOy8UvqkJRIZEcQD8Gf3wcq5cPpvbdVMa9F5MEz4M2z61I4Y2ZiiLXbojb8PhAW/geSucNkL8LPv4aRbPA89rFSgDLwQEFj9Onz5F5dqo46B2X9KH9vZNHexPWnyM+2n0Ijs3aWc0KtT8AJY9oL9Eh5/deuciH7EVHsh/NM/2gvg3UcfXFffemrRY7Z6SCJs/e3om7WKSIWW5C62z8CSObB/T2CqjdwNvtiWEgac7/ddaVLworSimp3FFcGbWGfjx3ac9T5nwPn/DO3qIm9E7BnVzu/tiKzTv4KYRHvGtegx23MzvqMd9nrUTYHpT6FUcwy+2M6rnNQlMNVGngwNzMCPmhS82JAfxJZHu3+AV66F9AFw6XN2XJTWKq69HR/p6XPghYugdLcdyC61v012x10emhfLlXI18EI7S9qZvw9ctVGQeE0KInKpMWaeiPQyxmwJZFChYEOwWh4V74AXL7MH06vmNX8E01DSdYRtRfHBr6HPmXDho/a+NZZ+VHhq1wl+8mWwowiIxkoKdwHzgNeAsKvkzc4rIy46gsyOATyLrSiGFy+1M6Bd/4GdzKatGD0dhk3Ri8ZKhbjGkkKRiHwG9BKR+e4rjTGBH6kpgLLzSslKTyIiUC2PaqvhlWtgz3q46tXD50ZuCzQhKBXyGksK52JLCC8AfwtMOKEjO6+UMX1TA7MzY+xF5c2f24Hs+pwemP0qpZSbxpLC08aYH4vIk8aYZo1XICITgIeASOApY8z9buu7A88BHZxtZhpjAtef24viA9XklVQG7iLzF3+xg72dNtMOZKeUUkHSWOe1ESLSA7hKRDqKSCfXW1NvLCKRwKPARGAgMEVEBrpt9jvgFWPM8cAVwGPN+zNaVkAvMi//L3x+n237PG6m//enlFKNaKyk8DjwAdAbWAq4Vq4bZ3ljTgA2GmM2A4jIXGASsMbtfeqb17QHdvocuR+tb0gKfi4pbPrMDmHRe5xtz6+tcZRSQea1pGCMedgYMwCYY4zpbYzp5XLzZRCarkCOy/NcZ5mru4GrRSQXeA/4mac3EpFpIrJERJYUFBT4sOujsyGvjISYSLp2iPffTvJW2wvLqf3tkNdRMf7bl1JK+ajJsY+MMTeLyFgRmQogIqki0suH9/Z02mvcnk8BnjXGdMNe2H5BRA6LyRjzhDFmpDFmZFpamg+7PjrZeaX0zUhC/HXmXrLTNj2NSYCrXtFWOUqpkNFkj2YR+T0wEugPPAPEAP8BxjTx0lzAdZD/bhxePXQDMAHAGLNQROKAVKAZ4y23nOy8Mk7v75Z8Nn1mu7l36m2bi2YMtvep/Y7sLL+y1HZOqyi2s6TpPMNKqRDiyzAXFwHHA8sAjDE7RcSXyvbFQJZTqtiBvZB8pds224EzgWdFZAAQB/i/fqgRRfur2FNWSf/Obn/ipk+gOAdik+Dbx6HWmSIvIspWAWUMOjRZJHU+/BpBbbUdviJ/jS0hdDkuMH+UUkr5yJekUGWMMSJiAETEp8kFjDE1InIrsADb3HSOMWa1iNwLLDHGzAd+ATwpIj/HVi1dZ4xxr2IKqPqJdQ4bCK9oC6T0tYO61VZD4SbIW2WvDeSttiN+/vDKwe3jOx2aJDIG2VEWN30CFzwCfccH8K9SSinf+JIUXhGRfwMdROQm4HrgSV/e3Olz8J7bslkuj9fQdDVUQHltjlq0+eAkL5HRkH6svQ255OA2B/ZC3honUTgJY9lzUF1+cJtTfwnDr/HzX6GUUs3TZFIwxvxVRM4CSrDXFWYZYz7ye2RBkp1XRlJsFJ2T4w4urKuzJYU+ZzT+4viO0HOMvbm+du8WmyBMrTNhh1JKhSZfh85eCcQ6j1f4KZaQkJ1XSlZG4qEtj0p3Qc2B5k0HGRFhZ05K6dNyQSqllJ802SRVRC4DvgMuBS4DvhWRSxp/VetkjCE7r/Twi8xFm+29zhGslGrjfCkp/BYYZYzJBxCRNOBj4FV/BhYMe8qq2FteTVa6JgWlVHhqsqQARNQnBEehj69rdTZ4G96iaBNExmifAqVUm+dLSeEDEVkAvOQ8vxx4338hBU92Yy2POvaEiMjAB6WUUgHkS+ujX4rIxcBY7NAVTxhj3vB7ZEGQnV9Gh3bRpCXFHrqiaItWHSmlwoIvw1z0At4zxrzuPI8XkZ7GmK3+Di7QsneX0i/dbcwjY2xJoddpwQtMKaUCxJdrA/OAOpfntc6yNqW+5VGWe9VR6W7b+ayTL2MAKqVU6+ZLUogyxlTVP3Eet7lxnvNLKympqPFwkVlbHimlwocvSaFARC6ofyIik4A9/gspOLK9tjzSpKCUCh++tD6aDrwoIrOd57nAj/0XUnCs391Iy6OIaGif6eFVSinVtvjS+mgTMFpEEgExxpT6P6zA25BXRkpCDCmJ7i2PNkHHHhDp64ggSinVevl8pDPGlPkzkGDLzvdwkRkOHR1VKaXauDbZM/lIGWPYmFd2+PUEY7SPglIqrGhSAHYVV1Ba6aHl0f4CqCqDTjrCqVIqPPgySmo7Efk/EXnSeZ4lIuf7P7TAWe+t5VHhJnuvJQWlVJjwpaTwDFAJnOQ8zwX+6LeIgqDR2dZAO64ppcKGL0mhjzHmL0A1gDHmAHYMpDYjO6+MtKRYOrRz65NXtBkkEjp0D05gSikVYL4khSoRiQcMgIj0wZYc2owNeaX0d686ApfmqNGBD0oppYLAl6Twe+ADIFNEXgQ+AX7l16gCqK7OkJ1Xps1RlVIK3zqvfSQiy4DR2Gqj24wxbWaYix37DnCgutZ7c9TME4MTmFJKBYEvrY8uAmqMMe8aY94BakTkQv+HFhheJ9YpL4TKEi0pKKXCik/VR8aY4vonxph92CqlNiE7z3bUzvLaHFX7KCilwodPczR7WNZmBgLakFdKl/ZxJMe5XUzW0VGVUmHIl6SwRET+LiJ9RKS3iPwDWOrvwAJlfV7p4aUEcJqjRmhzVKVUWPElKfwMqAJexs64VgHc4s+gAqW2zrAxv4x+6V5aHnXoDlFtbj4hpZTyypfWR/uBmQGIJeByisqprKk7vOUR2D4KWnWklAozXpOCiPzTGHO7iLyN03HNlTHmAg8va1UaWh519tActXAzHHdpEKJSSqngaayk8IJz/9dABBIM9Ukhy7366MBeqCzWkoJSKux4TQrGmKXO/RcikuY8LghUYIGQnVdG1w7xJMS6fQwNLY+0OapSKrx4vdAs1t0isgdYB2SLSIGIzApceP6VnVd6eKc10CGzlVJhq7HWR7cDY4BRxpgUY0xH4ERgjIj8PCDR+VFNbR2bC/Yffj0BnJKC2MHwlFIqjDSWFK4BphhjttQvMMZsBq521rVqWwvLqaqto1+6l6TQPhOiYgMfmFJKBVFjSSHa08B3znWFVj+W9AZvs62BTQopWnWklAo/jSWFqmauaxWy88oQgb4eO65pHwWlVHhqrEnqUBEp8bBcgDg/xRMw2fmlZHZsR3xM5KEryotsk1RNCkqpMNRYk9RIb+t8JSITgIeASOApY8z9buv/AZzuPG0HpBtjOhztfn2xIa/Uc9XRXucSiiYFpVQY8ttopyISCTwKnAXkAotFZL4xZk39NsaYn7ts/zPgeH/F46qqxrY8Gj8g4/CVhdpHQSkVvnwZEK+5TgA2GmM2G2OqgLnApEa2nwK85Md4Gmwt3E9NnfF+kRmBjj0DEYpSSoUUfyaFrkCOy/NcZ9lhRKQH0Av41I/xNGgY3sLbvMzJXSG61V82UUqpI+bPpCAelh02sJ7jCuBVY0ytxzcSmSYiS0RkSUHB0Y+0kZ1XRoRAnzQvSaFTr6Peh1JKtUb+TAq5QKbL827ATi/bXkEjVUfGmCeMMSONMSPT0tKOOrDs3aX0TEkgLtrDtfSiTZCi1xOUUuHJn0lhMZAlIr1EJAZ74J/vvpGI9Ac6Agv9GMshsvNLPVcdHdgH5YXa8kgpFbb8lhSMMTXArcACYC3wijFmtYjcKyKuczFMAeYaY7xVLbWoyppathWWa3NUpZTywG9NUgGMMe8B77ktm+X2/G5/xuBuc8F+ahtteYQ2R1VKhS1/Vh+FpOzGxjyq76OgzVGVUmEqLJNCVITQKzXh8JVFmyHpGIhpF/jAlFIqBIRhUiijZ2oCMVEe/vSizXo9QSkV1sIuKWzwNtsa6JDZSqmwF1ZJoaK6lm1FXloeVZTA/nwtKSilwlpYJYWN+WUY4+UiszZHVUqp8EoKB1seeRneAjQpKKXCWpglhTKiI4UeKR5aHhVusveaFJRSYSysksKGvFL6pCUSHemp5dEWSOwMMR4ShlJKhYmwSgp2zCMP1xNAm6MqpRRhlBT2V9aQU3SAfumNNEfVpKCUCnNhkxQ25pcBeC4pVJZB2W7to6CUCnthkxQabXmkzVGVUgoIo6QAkJWe6LnlkTZHVUopwM9DZ4eSS0dmcunITM8rNSkopRQQZiUFrwo3QUI6xHppmaSUUmFCkwLYPgpaSlBKKU0KgDZHVUophyaFqnIo3anNUZVSCk0K2hxVKaVcaFLQlkdKKdVAk4ImBaWUaqBJoXATtEuFuPbBjkQppYJOk4K2PFJKqQaaFLSPglJKNQjvpFB9AEpyNSkopZQjvJPC3q32PqVPUMNQSqlQEd5JoaHlUa/gxqGUUiFCkwJo9ZFSSjk0KcR3tDellFJhnhQKN0EnvZ6glFL1wjspaHNUpZQ6RPgmhZpKKM7RpKCUUi7CNyns3QYYbY6qlFIuwjcpFG2y91pSUEqpBmGcFLQ5qlJKuQvvpBDXXpujKqWUi/BOCp36gEiwI1FKqZDh16QgIhNEZL2IbBSRmV62uUxE1ojIahH5rz/jOUThJq06UkopN1H+emMRiQQeBc4CcoHFIjLfGLPGZZss4C5gjDFmr4ik+yueQ9RU2eaox10ekN0ppVRr4c+SwgnARmPMZmNMFTAXmOS2zU3Ao8aYvQDGmHw/xnPQvu1g6rSkoJRSbvyZFLoCOS7Pc51lrvoB/UTkfyKySEQmeHojEZkmIktEZElBQcHRR1bfHFX7KCil1CH8mRQ8XcE1bs+jgCxgHDAFeEpEOhz2ImOeMMaMNMaMTEtLO/rItDmqUkp55M+kkAtkujzvBuz0sM1bxphqY8wWYD02SfhX0WaITYZ2KX7flVJKtSb+TAqLgSwR6SUiMcAVwHy3bd4ETgcQkVRsddJmP8ZkFW22E+toc1SllDqE35KCMaYGuBVYAKwFXjHGrBaRe0XkAmezBUChiKwBPgN+aYwp9FdMDXTIbKWU8shvTVIBjDHvAe+5LZvl8tgAdzi3wKittq2PBk8O2C6VUqq1CL8ezfu2g6nVi8xKKeVB+CWFoi32XpujKqXUYcIwKeiQ2Uop5U0YJoXNEJMICS3Q30EppdqY8EwK2hxVKaU8CtOkoNcTlFLKk/BKCrU1sHerXk9QSikvwispFOdAXY0mBaWU8iK8koIOhKeUUo0Kz6SgfRSUUsqj8EsK0e0gMSPYkSilVEgKv6TQqbc2R1VKKS/CMCn0CnYUSikVssInKdTVOs1R9XqCUkp5Ez5JoTgXaqu05ZFSSjUifJKCNkdVSqkmaVJQSinVIHySQlJn6H8eJHUJdiRKKRWy/DodZ0g59jx7U0op5VX4lBSUUko1SZOCUkqpBpoUlFJKNdCkoJRSqoEmBaWUUg00KSillGqgSUEppVQDTQpKKaUaiDEm2DEcEREpALY18+WpwJ4WDKelaXxHR+M7eqEeo8bXfD2MMWlNbdTqksLREJElxpiRwY7DG43v6Gh8Ry/UY9T4/E+rj5RSSjXQpKCUUqpBuCWFJ4IdQBM0vqOj8R29UI9R4/OzsLqmoJRSqnHhVlJQSinVCE0KSimlGrTJpCAiE0RkvYhsFJGZHtbHisjLzvpvRaRnAGPLFJHPRGStiKwWkds8bDNORIpFZLlzmxWo+Jz9bxWRH5x9L/GwXkTkYefzWykiwwMYW3+Xz2W5iJSIyO1u2wT88xOROSKSLyKrXJZ1EpGPRGSDc9/Ry2uvdbbZICLXBii2B0VknfP/e0NEOnh5baPfBT/HeLeI7HD5P57r5bWN/t79GN/LLrFtFZHlXl4bkM+wxRhj2tQNiAQ2Ab2BGGAFMNBtm58CjzuPrwBeDmB8XYDhzuMkINtDfOOAd4L4GW4FUhtZfy7wPiDAaODbIP6vd2M75QT18wNOBYYDq1yW/QWY6TyeCTzg4XWdgM3OfUfncccAxHY2EOU8fsBTbL58F/wc493AnT58Bxr9vfsrPrf1fwNmBfMzbKlbWywpnABsNMZsNsZUAXOBSW7bTAKecx6/CpwpIhKI4Iwxu4wxy5zHpcBaoGsg9t2CJgHPG2sR0EFEgjH59ZnAJmNMc3u4txhjzJdAkdti1+/Zc8CFHl56DvCRMabIGLMX+AiY4O/YjDEfGmNqnKeLgG4tuc8j5eXz84Uvv/ej1lh8zrHjMuCllt5vMLTFpNAVyHF5nsvhB92GbZwfRjGQEpDoXDjVVscD33pYfZKIrBCR90VkUEADAwN8KCJLRWSah/W+fMaBcAXef4jB/PzqZRhjdoE9GQDSPWwTCp/l9diSnydNfRf87VanimuOl+q3UPj8TgHyjDEbvKwP9md4RNpiUvB0xu/e7taXbfxKRBKB14DbjTElbquXYatEhgKPAG8GMjZgjDFmODARuEVETnVbHwqfXwxwATDPw+pgf35HIqifpYj8FqgBXvSySVPfBX/6F9AHGAbswlbRuAv6dxGYQuOlhGB+hkesLSaFXCDT5Xk3YKe3bUQkCmhP84quzSIi0diE8KIx5nX39caYEmNMmfP4PSBaRFIDFZ8xZqdznw+8gS2iu/LlM/a3icAyY0ye+4pgf34u8uqr1Zz7fA/bBO2zdC5qnw9cZZzKb3c+fBf8xhiTZ4ypNcbUAU962XdQv4vO8eNi4GVv2wTzM2yOtpgUFgNZItLLOZu8Apjvts18oL6VxyXAp95+FC3NqX98GlhrjPm7l20611/jEJETsP+nwgDFlyAiSfWPsRckV7ltNh+4xmmFNBoorq8mCSCvZ2fB/PzcuH7PrgXe8rDNAuBsEenoVI+c7SzzKxGZAPwauMAYU+5lG1++C/6M0fU61UVe9u3L792fxgPrjDG5nlYG+zNslmBf6fbHDds6JhvbKuG3zrJ7sT8AgDhstcNG4DugdwBjG4st3q4Elju3c4HpwHRnm1uB1diWFIuAkwMYX29nvyucGOo/P9f4BHjU+Xx/AEYG+P/bDnuQb++yLKifHzZB7QKqsWevN2CvU30CbHDuOznbjgSecnnt9c53cSMwNUCxbcTWxdd/B+tb4x0DvNfYdyGAn98LzvdrJfZA38U9Ruf5Yb/3QMTnLH+2/nvnsm1QPsOWuukwF0oppRq0xeojpZRSzaRJQSmlVANNCkoppRpoUlBKKdVAk4JSSqkGmhSUciMitW4jsbbYyJsi0tN1pE2lQk1UsANQKgQdMMYMC3YQSgWDlhSU8pEzLv4DIvKdc+vrLO8hIp84A7d9IiLdneUZzlwFK5zbyc5bRYrIk2Ln0/hQROKD9kcp5UaTglKHi3erPrrcZV2JMeYEYDbwT2fZbOxQ4sdhB5Z72Fn+MPCFsQPzDcf2aAXIAh41xgwC9gGT/fz3KOUz7dGslBsRKTPGJHpYvhU4wxiz2RnUcLcxJkVE9mCHYKh2lu8yxqSKSAHQzRhT6fIePbHzJ2Q5z38NRBtj/uj/v0yppmlJQakjY7w89raNJ5Uuj2vRa3sqhGhSUOrIXO5yv9B5/A12dE6Aq4CvncefADcDiEikiCQHKkilmkvPUJQ6XLzbJOwfGGPqm6XGisi32BOqKc6yGcAcEfklUABMdZbfBjwhIjdgSwQ3Y0faVCpk6TUFpXzkXFMYaYzZE+xYlPIXrT5SSinVQEsKSimlGmhJQSmlVANNCkoppRpoUlBKKdVAk4JSSqkGmhSUUko1+H9L2FzeX77Q4gAAAABJRU5ErkJggg==\n", + "image/png": 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", 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+scikit-image +keras +tensorflow +matplotlib +gradio +pydicom \ No newline at end of file diff --git a/src/config/config.py b/src/config/config.py new file mode 100644 index 0000000000000000000000000000000000000000..004d56054ffcf55d9e112bad4154d7acadc50e7f --- /dev/null +++ b/src/config/config.py @@ -0,0 +1,7 @@ +from keras import backend as K + +K.set_image_data_format('channels_last') + +IMG_ROWS = int(512/2) +IMG_COLS = int(512/2) +SMOOTH = 1. \ No newline at end of file diff --git a/data.py b/src/data/data_loader.py similarity index 77% rename from data.py rename to src/data/data_loader.py index b7a36bce76e7ce8348c8c615ccc457a625f97c61..c7cf2dc5dbdcc63072cb65ad1ed4188115383bea 100755 --- a/data.py +++ b/src/data/data_loader.py @@ -3,7 +3,7 @@ import numpy as np import nibabel from skimage.io import imsave, imread -data_path = 'raw/' +data_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'data', 'raw') image_rows = int(512/2) image_cols = int(512/2) #we will undersample our training 2D images later (for memory and speed) @@ -44,15 +44,14 @@ def create_train_data(): for index,img in enumerate(imgsliv_train): imgs_mask[index,:,:]=img - - np.save('imgs_train.npy', imgs) - np.save('imgsliv_train.npy', imgs_mask) + np.save(os.path.join(data_path, '..', 'processed', 'imgs_train.npy'), imgs) + np.save(os.path.join(data_path, '..', 'processed', 'imgsliv_train.npy'), imgs_mask) print('Saving to .npy files done.') def load_train_data(): - imgs_train = np.load('imgs_train.npy') - imgs_mask_train = np.load('imgsliv_train.npy') + imgs_train = np.load(os.path.join(data_path, '..', 'processed', 'imgs_train.npy')) + imgs_mask_train = np.load(os.path.join(data_path, '..', 'processed', 'imgsliv_train.npy')) return imgs_train, imgs_mask_train @@ -75,8 +74,6 @@ def create_test_data(): elif 'orig' in image_name: imgs_test.append(dimg) - - imgst= np.ndarray((len(imgs_test), image_rows, image_cols), dtype=np.uint8) imgs_maskt= np.ndarray((len(imgsliv_test), image_rows, image_cols), dtype=np.uint8) @@ -85,15 +82,14 @@ def create_test_data(): for index,img in enumerate(imgsliv_test): imgs_maskt[index,:,:]=img - np.save('imgs_test.npy', imgst) - np.save('imgsliv_test.npy', imgs_maskt) + np.save(os.path.join(data_path, '..', 'processed', 'imgs_test.npy'), imgst) + np.save(os.path.join(data_path, '..', 'processed', 'imgsliv_test.npy'), imgs_maskt) print('Saving to .npy files done.') - def load_test_data(): - imgst = np.load('imgs_test.npy') - imgs_id = np.load('imgsliv_test.npy') + imgst = np.load(os.path.join(data_path, '..', 'processed', 'imgs_test.npy')) + imgs_id = np.load(os.path.join(data_path, '..', 'processed', 'imgsliv_test.npy')) return [imgst, imgs_id] if __name__ == '__main__': diff --git a/src/data/data_processing.py b/src/data/data_processing.py new file mode 100644 index 0000000000000000000000000000000000000000..f832301115b5c7d833e09c6fa9bbd981d76e57f8 --- /dev/null +++ b/src/data/data_processing.py @@ -0,0 +1,22 @@ +import numpy as np +from skimage.transform import resize +from src.config.config import IMG_ROWS, IMG_COLS +from src.data.data_loader import load_train_data, load_test_data + +def preprocess(imgs): + imgs_p = np.ndarray((imgs.shape[0], IMG_ROWS, IMG_COLS), dtype=np.uint8) + for i in range(imgs.shape[0]): + imgs_p[i] = resize(imgs[i], (IMG_COLS, IMG_ROWS), preserve_range=True) + imgs_p = imgs_p[..., np.newaxis] + return imgs_p + +def load_and_preprocess_train_data(): + imgs_train, imgs_mask_train = load_train_data() + imgs_train = preprocess(imgs_train) + imgs_mask_train = preprocess(imgs_mask_train) + return imgs_train, imgs_mask_train + +def load_and_preprocess_test_data(): + imgs_test, imgs_id_test = load_test_data() + imgs_test = preprocess(imgs_test) + return imgs_test, imgs_id_test \ No newline at end of file diff --git a/src/dicom_handler/dicom_predictor.py b/src/dicom_handler/dicom_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..87195f8fd478df0b41b66a226d81042aefed74f9 --- /dev/null +++ b/src/dicom_handler/dicom_predictor.py @@ -0,0 +1,22 @@ +import numpy as np +from skimage.segmentation import mark_boundaries +from ..model.model import get_unet +from .dicom_utils import load_and_preprocess_dicom + +def predict_dicom(dicom_path, weights_path, mean=0, std=1): + """Predict liver segmentation for a single DICOM file.""" + # Load and preprocess the DICOM file + img = load_and_preprocess_dicom(dicom_path) + img = (img - mean) / std + + # Load the model and weights + model = get_unet() + model.load_weights(weights_path) + + # Predict + mask = model.predict(np.expand_dims(img, axis=0))[0] + mask = (mask > 0.5).astype('uint8') + + # Overlay the mask on the original image + segmented = mark_boundaries(img, mask[:,:,0], color=(1,0,0), mode='thick') + return segmented \ No newline at end of file diff --git a/src/dicom_handler/dicom_utils.py b/src/dicom_handler/dicom_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2fe8d4e5ac613edfda82a2651203d2cd5d4f2399 --- /dev/null +++ b/src/dicom_handler/dicom_utils.py @@ -0,0 +1,23 @@ +import pydicom +import numpy as np +from skimage.transform import resize + +def load_dicom(file_path): + """Load a DICOM file and return it as a numpy array.""" + dicom = pydicom.dcmread(file_path) + return dicom.pixel_array + +def preprocess_dicom(dicom_array, target_size=(256, 256)): + """Preprocess the DICOM array by resizing and normalizing.""" + # Resize the image + resized = resize(dicom_array, target_size, mode='constant', preserve_range=True) + + # Normalize the image + normalized = (resized - np.min(resized)) / (np.max(resized) - np.min(resized)) + + return normalized.astype(np.float32) + +def load_and_preprocess_dicom(file_path, target_size=(256, 256)): + """Load a DICOM file, preprocess it, and return as a numpy array.""" + dicom_array = load_dicom(file_path) + return preprocess_dicom(dicom_array, target_size) \ No newline at end of file diff --git a/src/model/model.py b/src/model/model.py new file mode 100644 index 0000000000000000000000000000000000000000..e67881f00b1950c592d689102eef06efc604966a --- /dev/null +++ b/src/model/model.py @@ -0,0 +1,58 @@ +from keras.models import Model +from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose +from keras.optimizers import Adam +from src.config.config import IMG_ROWS, IMG_COLS, SMOOTH +from keras import backend as K + +def dice_coef(y_true, y_pred): + y_true_f = K.flatten(y_true) + y_pred_f = K.flatten(y_pred) + intersection = K.sum(y_true_f * y_pred_f) + return (2. * intersection + SMOOTH) / (K.sum(y_true_f) + K.sum(y_pred_f) + SMOOTH) + +def dice_coef_loss(y_true, y_pred): + return -dice_coef(y_true, y_pred) + +def get_unet(): + inputs = Input((IMG_ROWS, IMG_COLS, 1)) + + conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs) + conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1) + pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) + + conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1) + conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2) + pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) + + conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2) + conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3) + pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) + + conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3) + conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4) + pool4 = MaxPooling2D(pool_size=(2, 2))(conv4) + + conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4) + conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5) + + up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3) + conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6) + conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6) + + up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3) + conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7) + conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7) + + up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3) + conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8) + conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8) + + up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3) + conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9) + conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9) + + conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9) + + model = Model(inputs=[inputs], outputs=[conv10]) + model.compile(optimizer=Adam(learning_rate=1e-3), loss=dice_coef_loss, metrics=[dice_coef]) + return model \ No newline at end of file diff --git a/src/nifti_handler/nifti_predictor.py b/src/nifti_handler/nifti_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..d24236306bdaabef3bc2075bb7f08d1caa3d2f22 --- /dev/null +++ b/src/nifti_handler/nifti_predictor.py @@ -0,0 +1,22 @@ +import numpy as np +from skimage.segmentation import mark_boundaries +from ..model.model import get_unet +from .nifti_utils import load_and_preprocess_nifti + +def predict_nifti(nifti_path, weights_path, mean=0, std=1): + """Predict liver segmentation for a single NIfTI file.""" + # Load and preprocess the NIfTI file + img = load_and_preprocess_nifti(nifti_path) + img = (img - mean) / std + + # Load the model and weights + model = get_unet() + model.load_weights(weights_path) + + # Predict + mask = model.predict(np.expand_dims(img, axis=0))[0] + mask = (mask > 0.5).astype('uint8') + + # Overlay the mask on the original image + segmented = mark_boundaries(img, mask[:,:,0], color=(1,0,0), mode='thick') + return segmented \ No newline at end of file diff --git a/src/nifti_handler/nifti_utils.py b/src/nifti_handler/nifti_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..454a8906a6f66e170dbf0a72f9511cd7c400a184 --- /dev/null +++ b/src/nifti_handler/nifti_utils.py @@ -0,0 +1,28 @@ +import nibabel as nib +import numpy as np +from skimage.transform import resize + +def load_nifti(file_path): + """Load a NIfTI file and return it as a numpy array.""" + nifti = nib.load(file_path) + return nifti.get_fdata() + +def preprocess_nifti(nifti_array, target_size=(256, 256)): + """Preprocess the NIfTI array by selecting a middle slice, resizing and normalizing.""" + # Select the middle slice if it's a 3D volume + if nifti_array.ndim == 3: + middle_slice = nifti_array.shape[2] // 2 + nifti_array = nifti_array[:, :, middle_slice] + + # Resize the image + resized = resize(nifti_array, target_size, mode='constant', preserve_range=True) + + # Normalize the image + normalized = (resized - np.min(resized)) / (np.max(resized) - np.min(resized)) + + return normalized.astype(np.float32) + +def load_and_preprocess_nifti(file_path, target_size=(256, 256)): + """Load a NIfTI file, preprocess it, and return as a numpy array.""" + nifti_array = load_nifti(file_path) + return preprocess_nifti(nifti_array, target_size) \ No newline at end of file diff --git a/src/predict/predict.py b/src/predict/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..91204161850f983872957cc2017352cab3399e39 --- /dev/null +++ b/src/predict/predict.py @@ -0,0 +1,42 @@ +import os +import numpy as np +from skimage.exposure import rescale_intensity +from skimage.segmentation import mark_boundaries +from skimage import io +from src.model.model import get_unet +from src.data.data_processing import load_and_preprocess_test_data + +def predict(model, mean, std): + print('-'*30) + print('Loading and preprocessing test data...') + print('-'*30) + imgs_test, imgs_id_test = load_and_preprocess_test_data() + + imgs_test = imgs_test.astype('float32') + imgs_test -= mean + imgs_test /= std + + print('-'*30) + print('Loading saved weights...') + print('-'*30) + model.load_weights('weights.h5') + + print('-'*30) + print('Predicting masks on test data...') + print('-'*30) + imgs_mask_test = model.predict(imgs_test, verbose=1) + np.save('imgs_mask_test.npy', imgs_mask_test) + + print('-' * 30) + print('Saving predicted masks to files...') + print('-' * 30) + pred_dir = 'preds' + if not os.path.exists(pred_dir): + os.mkdir(pred_dir) + + for k in range(len(imgs_mask_test)): + a = rescale_intensity(imgs_test[k][:,:,0], out_range=(-1,1)) + b = (imgs_mask_test[k][:,:,0]).astype('uint8') + io.imsave(os.path.join(pred_dir, str(k) + '_pred.png'), mark_boundaries(a,b)) + + return imgs_mask_test \ No newline at end of file diff --git a/src/train/train.py b/src/train/train.py new file mode 100644 index 0000000000000000000000000000000000000000..02cef211e5a3c861a4c9196a6e36c00ba42d17bd --- /dev/null +++ b/src/train/train.py @@ -0,0 +1,40 @@ +import numpy as np +from keras.callbacks import ModelCheckpoint +from src.model.model import get_unet +from src.data.data_processing import load_and_preprocess_train_data + +def train_model(): + print('-'*30) + print('Loading and preprocessing train data...') + print('-'*30) + imgs_train, imgs_mask_train = load_and_preprocess_train_data() + + imgs_train = imgs_train.astype('float32') + mean = np.mean(imgs_train) + std = np.std(imgs_train) + + # save mean and std + np.save('mean.npy', mean) + np.save('std.npy', std) + + imgs_train -= mean + imgs_train /= std + + imgs_mask_train = imgs_mask_train.astype('float32') + + print('-'*30) + print('Creating and compiling model...') + print('-'*30) + model = get_unet() + model_checkpoint = ModelCheckpoint('../../models/weights.h5', monitor='val_loss', save_best_only=True) + + print('-'*30) + print('Fitting model...') + print('-'*30) + history = model.fit(imgs_train, imgs_mask_train, batch_size=10, epochs=20, verbose=1, shuffle=True, + validation_split=0.2, callbacks=[model_checkpoint]) + + return model, history, mean, std + +if __name__ == '__main__': + train_model() \ No newline at end of file diff --git a/src/utils/utils.py b/src/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0f18a18cbbf66df64801f47db1a18a4182b211f9 --- /dev/null +++ b/src/utils/utils.py @@ -0,0 +1,10 @@ +import matplotlib.pyplot as plt + +def plot_training_history(history): + plt.plot(history.history['dice_coef']) + plt.plot(history.history['val_dice_coef']) + plt.title('Model dice coeff') + plt.ylabel('Dice coeff') + plt.xlabel('Epoch') + plt.legend(['Train', 'Test'], loc='upper left') + plt.show() \ No newline at end of file