Image Segmentation using U-Net, U-Net with skip connections and M-Net architectures

Overview

Brain-Image-Segmentation

Segmentation of brain tissues in MRI image has a number of applications in diagnosis, surgical planning, and treatment of brain abnormalities. However, it is a time-consuming task to be performed by medical experts. In addition to that, it is challenging due to intensity overlap between the different tissues caused by the intensity homogeneity and artifacts inherent to MRI. Tominimize this effect, it was proposed to apply histogram based preprocessing. The goal of this project was to develop a robust and automatic segmentation of the human brain.

To tackle the problem, I have used a Convolutional Neural Network (CNN) based approach. U-net is one of the most commonly used and best-performing architecture in medical image segmentation. This moodel consists of the 2-D implementation of the U-Net.The performance was evaluated using Dice Coefficient (DSC).

Dataset

This model was built for the following dataset: https://figshare.com/articles/brain_tumor_dataset/1512427

3064 T1-weighted contrast-inhanced images with three kinds of brain tumor are provided in the dataset.The three types of tumor are

1.Glioma 2.Pituitary Tumor 3.Meningioma

dataset

Model Architecture

The first half of the U-net is effectively a typical convolutional neural network like one would construct for an image classification task, with successive rounds of zero-padded ReLU-activated convolutions and ReLU-activated max-pooling layers. Instead of classification occurring at the "bottom" of the U, symmetrical upsampling and convolution layers are used to bring the pixel-wise prediction layer back to the original dimensions of the input image.

Here is the architecture for the 2D U-Net from the original publication mentioned earlier:

u-net-architecture

Here's an example of the correlation between my predictions in a single 2D plane:

Example 1: Example 2:
ground truth prediction

Libraries Used

The code has been tested with the following configuration

  • h5py == 2.10.0
  • keras == 2.3.1
  • scipy == 0.19.0
  • sckit-learn == 0.18.1
  • tensorflow == 2.2.0
  • tgpu == NVIDIA Tesla K80 (Google Colab)

The U-Net was based on this paper: https://arxiv.org/abs/1802.10508

Tips for improving model:

-The feature maps have been reduced so that the model will train using under 12GB of memory. If you have more memory to use, consider increasing the feature maps this will increase the complexity of the model (which will also increase its memory footprint but decrease its execution speed).

-If you choose a subset with larger tensors (e.g. liver or lung), it is recommended to add another maxpooling level (and corresponding upsampling) to the U-Net model. This will of course increase the memory requirements and decrease execution speed, but should give better results because it considers an additional recepetive field/spatial size.

-Consider different loss functions. The default loss function here is a binary_crossentropy. Different loss functions yield different loss curves and may result in better accuracy. However, you may need to adjust the learning_rate and number of epochs to train as you experiment with different loss functions.

-Try exceuting other U-Net architectures in the 2d/model folders.

Owner
Angad Bajwa
Angad Bajwa
A Pytorch implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

SMU_pytorch A Pytorch Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/ab

Fuhang 36 Dec 24, 2022
deep learning model with only python and numpy with test accuracy 99 % on mnist dataset and different optimization choices

deep_nn_model_with_only_python_100%_test_accuracy deep learning model with only python and numpy with test accuracy 99 % on mnist dataset and differen

0 Aug 28, 2022
Pytorch implementation of MaskFlownet

MaskFlownet-Pytorch Unofficial PyTorch implementation of MaskFlownet (https://github.com/microsoft/MaskFlownet). Tested with: PyTorch 1.5.0 CUDA 10.1

Daniele Cattaneo 84 Nov 02, 2022
This repository contains the code for the paper "Hierarchical Motion Understanding via Motion Programs"

Hierarchical Motion Understanding via Motion Programs (CVPR 2021) This repository contains the official implementation of: Hierarchical Motion Underst

Sumith Kulal 40 Dec 05, 2022
Implementation of Continuous Sparsification, a method for pruning and ticket search in deep networks

Continuous Sparsification Implementation of Continuous Sparsification (CS), a method based on l_0 regularization to find sparse neural networks, propo

Pedro Savarese 23 Dec 07, 2022
This repo provides code for QB-Norm (Cross Modal Retrieval with Querybank Normalisation)

This repo provides code for QB-Norm (Cross Modal Retrieval with Querybank Normalisation) Usage example python dynamic_inverted_softmax.py --sims_train

36 Dec 29, 2022
An end-to-end project on customer segmentation

End-to-end Customer Segmentation Project Note: This project is in progress. Tools Used in This Project Prefect: Orchestrate workflows hydra: Manage co

Ocelot Consulting 8 Oct 06, 2022
Norm-based Analysis of Transformer

Norm-based Analysis of Transformer Implementations for 2 papers introducing to analyze Transformers using vector norms: Kobayashi+'20 Attention is Not

Goro Kobayashi 52 Dec 05, 2022
Keras code and weights files for popular deep learning models.

Trained image classification models for Keras THIS REPOSITORY IS DEPRECATED. USE THE MODULE keras.applications INSTEAD. Pull requests will not be revi

François Chollet 7.2k Dec 29, 2022
CS550 Machine Learning course project on CNN Detection.

CNN Detection (CS550 Machine Learning Project) Team Members (Tensor) : Yadava Kishore Chodipilli (11940310) Thashmitha BS (11941250) This is a work do

yaadava_kishore 2 Jan 30, 2022
Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided curriculum Learning Approach

Get Fooled for the Right Reason Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness throu

Sowrya Gali 1 Apr 25, 2022
Code for HLA-Face: Joint High-Low Adaptation for Low Light Face Detection (CVPR21)

HLA-Face: Joint High-Low Adaptation for Low Light Face Detection The official PyTorch implementation for HLA-Face: Joint High-Low Adaptation for Low L

Wenjing Wang 77 Dec 08, 2022
Code I use to automatically update my videos' metadata on YouTube

mCodingYouTube This repository contains the code I use to automatically update my videos' metadata on YouTube, including: titles, descriptions, tags,

James Murphy 19 Oct 07, 2022
A Vision Transformer approach that uses concatenated query and reference images to learn the relationship between query and reference images directly.

A Vision Transformer approach that uses concatenated query and reference images to learn the relationship between query and reference images directly.

24 Dec 13, 2022
Predicting path with preference based on user demonstration using Maximum Entropy Deep Inverse Reinforcement Learning in a continuous environment

Preference-Planning-Deep-IRL Introduction Check my portfolio post Dependencies Gym stable-baselines3 PyTorch Usage Take Demonstration python3 record.

Tianyu Li 9 Oct 26, 2022
A simple library that implements CLIP guided loss in PyTorch.

pytorch_clip_guided_loss: Pytorch implementation of the CLIP guided loss for Text-To-Image, Image-To-Image, or Image-To-Text generation. A simple libr

Sergei Belousov 74 Dec 26, 2022
A PyTorch implementation of the paper "Semantic Image Synthesis via Adversarial Learning" in ICCV 2017

Semantic Image Synthesis via Adversarial Learning This is a PyTorch implementation of the paper Semantic Image Synthesis via Adversarial Learning. Req

Seonghyeon Nam 146 Nov 25, 2022
这是一个利用facenet和retinaface实现人脸识别的库,可以进行在线的人脸识别。

Facenet+Retinaface:人脸识别模型在Keras当中的实现 目录 注意事项 Attention 所需环境 Environment 文件下载 Download 预测步骤 How2predict 参考资料 Reference 注意事项 该库中包含了两个网络,分别是retinaface和fa

Bubbliiiing 31 Nov 15, 2022
A Domain-Agnostic Benchmark for Self-Supervised Learning

DABS: A Domain Agnostic Benchmark for Self-Supervised Learning This repository contains the code for DABS, a benchmark for domain-agnostic self-superv

Alex Tamkin 81 Dec 09, 2022
Shape-Adaptive Selection and Measurement for Oriented Object Detection

Source Code of AAAI22-2171 Introduction The source code includes training and inference procedures for the proposed method of the paper submitted to t

houliping 24 Nov 29, 2022