Segmentation and Identification of Vertebrae in CT Scans using CNN, k-means Clustering and k-NN

Overview

Segmentation and Identification of Vertebrae in CT Scans using CNN, k-means Clustering and k-NN

If you use this code for your research, please cite our paper:

@Article{informatics8020040,
AUTHOR = {Altini, Nicola and De Giosa, Giuseppe and Fragasso, Nicola and Coscia, Claudia and Sibilano, Elena and Prencipe, Berardino and Hussain, Sardar Mehboob and Brunetti, Antonio and Buongiorno, Domenico and Guerriero, Andrea and Tatò, Ilaria Sabina and Brunetti, Gioacchino and Triggiani, Vito and Bevilacqua, Vitoantonio},
TITLE = {Segmentation and Identification of Vertebrae in CT Scans Using CNN, k-Means Clustering and k-NN},
JOURNAL = {Informatics},
VOLUME = {8},
YEAR = {2021},
NUMBER = {2},
ARTICLE-NUMBER = {40},
URL = {https://www.mdpi.com/2227-9709/8/2/40},
ISSN = {2227-9709},
DOI = {10.3390/informatics8020040}
}

Graphical Abstract: GraphicalAbstract


Materials

Dataset can be downloaded for free at this URL.


Configuration and pre-processing

Configure the file config/paths.py according to paths in your computer. Kindly note that base_dataset_dir should be an absolute path which points to the directory which contains the subfolders with images and labels for training and validating the algorithms present in this repository.

In order to perform pre-processing, execute the following scripts in the given order.

  1. Perform Train / Test split:
python run/task0/split.py --original-training-images=OTI --original-training-labels=OTL \ 
                          --original-validation-images=OVI --original-validation-labels=OVL

Where:

  • OTI is the path with the CT scan from the original dataset (downloaded from VerSe challenge, see link above);
  • OTL is the path with the labels related to the original dataset;
  • OVI is the path where test images will be put;
  • OVL is the path where test labels will be put.
  1. Cropping the splitted datasets:
python run/task0/crop_mask.py --original-training-images=OTI --original-training-labels=OTL \ 
                              --original-validation-images=OVI --original-validation-labels=OVL

Where the arguments are the same of 1).

  1. Pre-processing the cropped datasets (see also Payer et al. pre-processing):
python run/task0/pre_processing.py

Binary Segmentation

In order to perform this stage, 3D V-Net has been exploited. The followed workflow for binary segmentation is depicted in the following figure:

BinarySegmentationWorkflowImage

Training

To perform the training, the syntax is as follows:

python run/task1/train.py --epochs=NUM_EPOCHS --batch=BATCH_SIZE --workers=NUM_WORKERS \
                          --lr=LR --val_epochs=VAL_EPOCHS

Where:

  • NUM_EPOCHS is the number of epochs for which training the CNN (we often used 500 or 1000 in our experiments);
  • BATCH_SIZE is the batch size (we often used 8 in our experiments, in order to benefit from BatchNormalization layers);
  • NUM_WORKERS is the number of workers in the data loading (see PyTorch documentation);
  • LR is the learning rate,
  • VAL_EPOCHS is the number of epochs after which performing validation during training (a checkpoint model is also saved every VAL_EPOCHS epochs).

Inference

To perform the inference, the syntax is as follows:

python run/task1/segm_bin.py --path_image_in=PATH_IMAGE_IN --path_mask_out=PATH_MASK_OUT

Where:

  • PATH_IMAGE_IN is the folder with input images;
  • PATH_MASK_OUT is the folder where to write output masks.

An example inference result is depicted in the following figure:

BinarySegmentationInferenceImage

Metrics Calculation

In order to calculate binary segmentation metrics, the syntax is as follows:

python run/task1/metrics.py

Multiclass Segmentation

The followed workflow for multiclass segmentation is depicted in the following figure:

MultiClassSegmentationWorkflow

To perform the Multiclass Segmentation (can be performed only on binary segmentation output), the syntax is as follows:

python run/task2/multiclass_segmentation.py --input-path=INPUT_PATH \
                                            --gt-path=GT_PATH \
                                            --output-path=OUTPUT_PATH \
                                            --use-inertia-tensor=INERTIA \
                                            --no-metrics=NOM

Where:

  • INPUT_PATH is the path to the folder containing the binary spine masks obtained in previous steps (or binary spine ground truth).
  • GT_PATH is the path to the folder containing ground truth labels.
  • OUTPUT_PATH is the path where to write the output multiclass masks.
  • INERTIA can be either 0 or 1 depending or not if you want to include inertia tensor in the feature set for discrminating between bodies and arches (useful for scoliosis cases); default is 0.
  • NOM can be either 0 or 1 depending or not if you want to skip the calculation of multi-Hausdorff distance and multi-ASSD for the vertebrae labelling (it can be very computationally expensive with this implementation); default is 1.

Figures highlighting the different steps involved in this stage follows:

  • Morphology MultiClassSegmentationMorphology

  • Connected Components MultiClassSegmentationConnectedComponents

  • Clustering and arch/body coupling MultiClassSegmentationClustering

  • Centroids computation MultiClassSegmentationCentroids

  • Mesh reconstruction MultiClassSegmentationMesh


Visualization of the Predictions

The base_dataset_dir folder also contains the outputs folders:

  • predTr contains the binary segmentation predictions performed on training set;
  • predTs contains the binary segmentation predictions performed on testing set;
  • predMulticlass contains the multiclass segmentation predictions and the JSON files containing the centroids' positions.

Implementation of the Chamfer Distance as a module for pyTorch

Chamfer Distance for pyTorch This is an implementation of the Chamfer Distance as a module for pyTorch. It is written as a custom C++/CUDA extension.

Christian Diller 205 Jan 05, 2023
Extract MNIST handwritten digits dataset binary file into bmp images

MNIST-dataset-extractor Extract MNIST handwritten digits dataset binary file into bmp images More info at http://yann.lecun.com/exdb/mnist/ Dependenci

Omar Mostafa 6 May 24, 2021
we propose EfficientDerain for high-efficiency single-image deraining

EfficientDerain we propose EfficientDerain for high-efficiency single-image deraining Requirements python 3.6 pytorch 1.6.0 opencv-python 4.4.0.44 sci

Qing Guo 126 Dec 07, 2022
Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"

BAM and CBAM Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)" Updat

Jongchan Park 1.7k Jan 01, 2023
A FAIR dataset of TCV experimental results for validating edge/divertor turbulence models.

TCV-X21 validation for divertor turbulence simulations Quick links Intro Welcome to TCV-X21. We're glad you've found us! This repository is designed t

0 Dec 18, 2021
Lightweight tool to perform MITM attack on local network

ARPSpy - A lightweight tool to perform MITM attack Using many library to perform ARP Spoof and auto-sniffing HTTP packet containing credential. (Never

MinhItachi 8 Aug 28, 2022
This is the official pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering" on VQA Task

🌈 ERASOR (RA-L'21 with ICRA Option) Official page of "ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point C

Hyungtae Lim 225 Dec 29, 2022
Privacy as Code for DSAR Orchestration: Privacy Request automation to fulfill GDPR, CCPA, and LGPD data subject requests.

Meet Fidesops: Privacy as Code for DSAR Orchestration A part of the greater Fides ecosystem. ⚡ Overview Fidesops (fee-dez-äps, combination of the Lati

Ethyca 44 Dec 06, 2022
2D&3D human pose estimation

Human Pose Estimation Papers [CVPR 2016] - 201511 [IJCAI 2016] - 201602 Other Action Recognition with Joints-Pooled 3D Deep Convolutional Descriptors

133 Jan 02, 2023
Contour-guided image completion with perceptual grouping (BMVC 2021 publication)

Contour-guided Image Completion with Perceptual Grouping Authors Morteza Rezanejad*, Sidharth Gupta*, Chandra Gummaluru, Ryan Marten, John Wilder, Mic

Sid Gupta 6 Dec 27, 2022
Facial recognition project

Facial recognition project documentation Project introduction This project is developed by linuxu. It is a face model recognition project developed ba

Jefferson 2 Dec 04, 2022
Airbus Ship Detection Challenge

Airbus Ship Detection Challenge This is an open solution to the Airbus Ship Detection Challenge. Our goals We are building entirely open solution to t

minerva.ml 55 Nov 29, 2022
Robot Servers and Server Manager software for robo-gym

robo-gym-server-modules Robot Servers and Server Manager software for robo-gym. For info on how to use this package please visit the robo-gym website

JR ROBOTICS 4 Aug 16, 2021
Ratatoskr: Worcester Tech's conference scheduling system

Ratatoskr: Worcester Tech's conference scheduling system In Norse mythology, Ratatoskr is a squirrel who runs up and down the world tree Yggdrasil to

4 Dec 22, 2022
Fairness Metrics: All you need to know

Fairness Metrics: All you need to know Testing machine learning software for ethical bias has become a pressing current concern. Recent research has p

Anonymous2020 1 Jan 17, 2022
A curated list of Generative Deep Art projects, tools, artworks, and models

Generative Deep Art A curated list of Generative Deep Art projects, tools, artworks, and models Inbox Get started with making AI art in 2022 – deeplea

Filipe Calegario 251 Jan 03, 2023
Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal Forecasting."

Spacetimeformer Multivariate Forecasting This repository contains the code for the paper, "Long-Range Transformers for Dynamic Spatiotemporal Forecast

QData 440 Jan 02, 2023
deep-prae

Deep Probabilistic Accelerated Evaluation (Deep-PrAE) Our work presents an efficient rare event simulation methodology for black box autonomy using Im

Safe AI Lab 4 Apr 17, 2021
DyNet: The Dynamic Neural Network Toolkit

The Dynamic Neural Network Toolkit General Installation C++ Python Getting Started Citing Releases and Contributing General DyNet is a neural network

Chris Dyer's lab @ LTI/CMU 3.3k Jan 06, 2023
A map update dataset and benchmark

MUNO21 MUNO21 is a dataset and benchmark for machine learning methods that automatically update and maintain digital street map datasets. Previous dat

16 Nov 30, 2022