Software

AFreeCA
Code supporting our ECCV 2024 paper: Annotation-Free Counting for All

(Adriano D’Alessandro)

BiasPruner
Code supporting our MICCAI 2024 paper: BiasPruner: Debiased Continual Learning for Medical Image Classification

(Nour Bayasi)

TrIND
Code supporting our MICCAI 2024 paper: Representing Anatomical Trees by Denoising Diffusion of Implicit Neural Fields

(Ashish Sinha)

LesionElevation
Code supporting our ISIC 2024 paper: Lesion Elevation Prediction from Skin Images Improves Diagnosis

(Kumar Abhishek)

StyleSeg
Code supporting our ISIC 2024 paper: Segmentation Style Discovery: Application to Skin Lesion Images

(Kumar Abhishek and Jeremy Kawahara)

SLiMe
Code supporting our ICL 2024 work, SLiMe: Segment-Like-Me paper

(Aliasghar Khani)

SyRaC
Code supporting our work on SYRAC: Synthesize, Rank, and Count

(Adriano D’Alessandro) 

SubPrecisionContactDetection (MCS-DETECT)

Code supporting our Journal of Cell Biology paper on automatic sub-precision membrane contact site detection, in our paper: Membrane contact site detection (MCS-DETECT) reveals dual control of rough mitochondria-ER contacts

(Ben Cardoen)

SPECHT

Code supporting our work on SPECHT: Self-tuning Plausibility Based Object Detection Enables Quantification of Conflict in Heterogeneous Multi-scale Microscopy

(Ben Cardoen)

WhiteNNer
Code supporting our ICCV VRMI paper on WhiteNNer-Blind Image Denoising via Noise Whiteness Priors

(Saeed Izadi)

Multimodal XAI

Code supporting our AAAI paper on Evaluating Explainable AI on Multi-Modal Medical Imaging Task

(Weina Jin)

Skin3D

Code supporting our Medical Image Analysis paper: Skin3D: Detection and Longitudinal Tracking of Pigmented Skin Lesions in 3D Total-Body Textured Meshes

(Mengliu Zhao, Jeremy Kawahara, Kumar Abhishek, and Sajjad Shamanian)

ERGO

Code supporting our IEEE TMI paper: ERGO: efficient recurrent graph optimized emitter density estimation in single molecule localization microscopy.

(Ben Cardoen) 

Learning-to-Augment Noisy & Denoised Data

Code supporting our paper Computers in Biology and Medicine 2021 paper: Learning-to-Augment Strategy using Noisy and Denoised Data: Improving Generalizability of Deep CNN for the Detection of COVID-19 in X-ray Images

(Mohammad Momeny) 

ImHistNet

Code supporting our papers  CMIG2021, MICCAI2019, MICCAI MLMI 2019 for 2D ImHistNet and 3D ImHistNet - Learnable Image Histogram-based DNN.

(Arafat Hussain) 

Generalizable Feature Learning with Class Imbalance

Code supporting our MICCAI 2019 paper: Generalizable Feature Learning in the Presence of Data Bias and Domain Class Imbalance: Application to Skin Lesion Classification

(Chris Yoon) 

MCC Loss for Segmentation

A loss function for training deep segmentation models based on the Matthews Correlation Coefficient (supporting our ISBI 2021 paper)

(Kumar Abhishek) 

SuperResNET

Super-Resolution NETwork Analysis, SuperResNET, is an integrated software for the analysis of 3D single molecule localization microscopy (SMLM) point cloud data.  It consists of computational modules to read, pre-process, post-process, quantify,  and visualize 3D SMLM data.  SuperResNET allows the user to extract, visualize, and analyze biological clusters.  SuperResNET is largely based on the original Scientific Reports 2018 and Scientific Reports 2019 works. 

(Ismail Khater) 

Scanner Invariant Deep Learning Segmentation

Code for ISBI 2020  paper:  Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI (video)

(Shahab Aslani) 

Kernelized Manifold Mapping

Code for CVPR 2019  paper: A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations

(Saeid Asgari and Kumar Abhishek) 

ComboLoss

Code for CMIG 2019 paper: Combo loss: Handling input and output imbalance in multi-organ segmentation

(Saeid Asgari) 

MM-GAN

Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network

(Anmol Sharma) 

Illumination-based Transformations for Skin Lesion Images

Generating transformations based on illumination information and color imaging of the skin for RGB skin lesion images.

(Kumar Abhishek) 

SMLMvis 

Superresolution visualization of 3D protein localization data from a range of super-resolution microscopes

(Ben Cardoen) 

AECNN

Code for AECNN: Adversarial and Enhanced CNN for data-efficient segmentation of gastrointestinal polyps from colonoscopy images

(Saeed Izadi)

GAN Skin Lesion Segmentation

Code for ISBI 2018 paper: Generative adversarial networks to segment skin lesions
(Saeed Izadi) 

Super-Resolution via Bilinear Pooling

Code for MICCAI 2019 MLMIR: Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy

(Saeed Izadi) 

Super-Resolution (Confocal Laser Endomicroscopy)

Code for MICCAI 2018: Can Deep Learning Relax Endomicroscopy Hardware Miniaturization Requirements?

(Saeed Izadi) 

skinisic 

Trained CNN to detect four types of dermoscopic criteria based on our winning entry for Part 2 of the 2017 ISIC Skin Challenge

(Jeremy Kawahara) 

SMLM PC3 Cav1/CAVIN1 Point Cloud Data

3D point clouds of super resolution molecule localization microscopy (SMLM) of Cav1 protein in prostate cancer (PC3) and CAVIN1/PTRF transfected PC3 (PC3-PTRF) cells

(Ismail Khater) 

Melanoma Recognition via Visual Attention 

Attention-based method for melanoma recognition, with attention  map regularization

(Yiqi Yan and Jeremy Kawahara) 

Local Synthetic Instances (LSI)

An oversampling method for synthetic data generation or similar tasks. It is designed for the challenging case of high-dimensional, non-gaussian data with low sample size

(Colin Brown) 

HistoGlimpseRNN

Recurrent Visual Attention Model for Analyzing Histopathology Whole Slide Images

(Aïcha Bentaieb) 

UncertaintyGuidedFCN

Uncertainty-guided Multi-loss Functions 

(Aïcha Bentaieb) 

A Structured Latent Model for Ovarian Carcinoma Subtyping from Histopathology Slides 

(Aïcha Bentaieb) 

BrainNetCNN 

Convolutional neural networks for connectomes 

(Jeremy Kawahara and Colin Brown)

PulsatileVascuSeg

Highlight vascular structures exhibiting radial distension pulsatile motions

(Alborz Amir-Khalili) 

TopoFCN

Topology-Aware Fully Convolutional Network for Image Segmentation

(Aïcha Bentaieb) 

dMRIGraphEmbed

Take a field of diffusion orientation distribution functions (ODF) and map them  to a graph representation

(Brian Booth) 

DijkstraTract

This minimal path tractography toolbox is a MATLAB tool to take a diffusion MRI (dMRI) scan and generate a graph representation where a each node is a voxel in the dMRI and graph edges are weighted by the diffusion measurements. This graph is then combined with Dijkstra's algorithm to perform minimal path tractography

(Brian Booth)

Reference:

• Brian G. Booth and Ghassan Hamarneh. Exact Integration of Diffusion Orientation Distribution Functions for Graph-Based Diffusion MRI Analysis. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 935-938, 2011.

DT-ICE 

MATLAB tool to generate information content measures from tensor-valued data.  Estimators are provided for entropy, joint entropy, conditional entropy, mutual information, and variation of information. Both Shannon and Renyi-based information content estimators are implemented.

(Brian Booth)

Reference:

• Brian G. Booth and Ghassan Hamarneh. Consistent Information Content Estimation for Diffusion Tensor MR Images. In IEEE Conference on Healthcare Informatics, Imaging and Systems Biology (IEEE HISB), pages 166-173, 2011.

WalkTract

MATLAB tool for competitive, multi-region, probabilistic tractography.

(Brian Booth) 

References:

• Brian G. Booth and Ghassan Hamarneh. Exact Integration of Diffusion Orientation Distribution Functions for Graph-Based Diffusion MRI Analysis. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 935-938, 2011.   

• Brian G. Booth and Ghassan Hamarneh. Global Multi-Region Competitive Tractography. In IEEE workshop on Mathematical Methods for Biomedical Image Analysis (IEEE MMBIA), pages 73-78, 2012.  

DT-STRUCT

MATLAB toolbox to highlight corners, tubular structures, and sheet-like structures in diffusion tensor images. In particular, DT-STRUCT can highlight corners, tubular structures, and sheet-like structures. Each structure detector can be run at multiple spatial scales.

(Brian Booth & Krishna Nand) 

Reference: 

• Krishna Nand, Rafeef Abugharbieh, Brian G. Booth, and Ghassan Hamarneh. Detecting Structure in Diffusion Tensor MR Images. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 6892, pages 90-97, 2011.  

DT-Frenet

MATLAB tool to segment highly-curved fiber bundles from DTI.

(Brian Booth) 

Reference:

• Brian G. Booth and Ghassan Hamarneh. A Cross-sectional Piecewise Constant Model for Segmenting Highly Curved Fiber Tracts in Diffusion MR Images. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 8151, pages 469-476, 2013.

STEAM

Statistical Template Estimation for Abnormality Mapping

(Brian Booth) 

CervCellSeg

MATLAB code for segmenting cervical cells, based on "A Variational Approach for Overlapping Cell Segmentation", which was submitted to the "Overlapping Cervical Cytology Image Segmentation Challenge", held in conjunction with IEEE International Symposium on Biomedical Imaging (IEEE ISBI), 2014.

(Masoud Nosrati) 

References:

• Masoud Nosrati and Ghassan Hamarneh. A Variational Approach for Overlapping Cell Segmentation. In Overlapping Cervical Cytology Image Segmentation Challenge, in conjunction with IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 1-2, 2014. 

• See also our newer related work: Masoud Nosrati and Ghassan Hamarneh. Segmentation of Overlapping Cervical Cells: A Variational Method with Star-Shape Prior. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 186-189, 2015.

View3D

MATLAB viewer for 3D scalar, vector, and tensor-valued medical images  

(Hossein Badakhshannoory) 

Documentation (PDF file)

CudaFitKMdPET

CUDA code for Fast GPU Fitting of Kinetic Models for Dynamic PET

(Ben Smith )

Reference: 

• Ben Smith, Ghassan Hamarneh, and Ahmed Saad. Fast GPU Fitting of Kinetic Models for Dynamic PET. In International Workshop on High-Performance Medical Image Computing for Image-Assisted Clinical Intervention and Decision-Making (MICCAI HP), pages 1-10, 2010

MRF Scale Selection

MRF based method for vessel scale selection

(Hengameh Mirzaliian) 

Reference:   

• Hengameh Mirzaalian and Ghassan Hamarneh. Vessel Scale Selection using MRF Optimization. In IEEE Computer Vision and Pattern Recognition (IEEE CVPR), pages 3273-3278, 2010.    

HairSim

Software for simulating hair on skin images for dermatology studies

(Hengameh Mirzaliian) 

ILR Toolkit

Software library for manipulating multi-region, probabilistic shapes using Aitchison geometry

(Shawn Andrews) 

TongueTrack

Software for tracking the movement of the tongue in ultrasound sequences

(Lisa Tang)  

VascuSynth

Simulate 3D images of vascular trees based on oxygen demand maps and physical parameters

(Preet Jassi) 

TurtleSeg

Interactive 3D image segmentation software with a unique "spotlight" feature

(Andrew Top)

PerceptVis

Perception-based visualization of manifold-valued medical images using distance preserving dimensionality reduction

(Chris McIntosh) 

FastRW

Fast version of random walker with priors

(Shawn Andrews) 

ITK   and  ITK Image IO  on Apple iOS 

Documentation and code for running ITK on iOS devices (iPod touch, iPhone, iPad)

(Boris Shabash & Zhi Feng Huang) 

LiveVessel

Intuitive and efficient vessel segmentation tool, finds centre-line and boundaries simultaneously

(Miranda Poon and Ryan Dickie)

NMQC

Scintillation camera quality control tests following NEMA procedures

(Andy Rova) 

ACO Shape Correspondence  [on TMW]

Ant Colony Optimization for Shape Correspondence

(Olive van Kaick)  

n-SIFT

n-dimensional scale invariant feature transform (SIFT)(3D SIFT, 4D SIFT, etc.) 

(Warren Cheung)  

I-DO

ITK Deformable Organisms framework

(Chris McIntosh)  

MATITK

MATLAB-ITK interface for calling ITK filters from MATLAB

(Vincent Chu)

Livewire

Matlab program for interactive 2D segmentation

• Download:

  • Win32:   Download Matlab code and a pre-compiled graph search C code for Windows 32bit.

  • Win64:   Same as above but for Windows 64

  • The following has been provided by Sven Holcombe:  You can download here a GUI interface for lwcontour, which includes:

    • The ability to undo previously placed seeds

    •  The ability to toggle between LWCONTOUR drawing and STRAIGHT drawing modes

    •  The ability to use lwcontour inside an already-existing figure window (ie, to incorporate it into other GUI layouts)

• AVI Videos:  Brain  &  Dijkstra

Reference: 

• A. Chodorowski, U. Mattsson, M. Langille, G. Hamarneh, "Color Lesion Boundary Detection Using Live Wire", Proceedings of SPIE Medical Imaging: Image Processing, vol. 5747, 2005, pp. 1589-1596.

Active Shape Models

Matlab program for 2D segmentation  of known shapes

Notes:

• Instructions (from Uppsala university)

• Example AVI movie

• Proposed corrections (not reviewed)

References:

• • G. Hamarneh, R. Abu-Gharbieh, T. Gustavsson. Review - Active Shape Models - Part I: Modeling Shape and Gray Level Variation. Swedish Symposium on Image Analysis, 1998, pp. 125-128.

  • R. Abu-Gharbieh, G. Hamarneh, T. Gustavsson. Review - Active Shape Models - Part II: Image Search and Classification. Swedish Symposium on Image Analysis, 1998, pp. 129-132.

  • G. Hamarneh, Ph.D. Thesis Towards Intelligent Deformable Models for Medical Image Analysis. Department of Signals and Systems, School of Electrical and Computer Engineering, Chalmers University of Technology, 2001. Technical report 415, ISBN 91-7291-082-8. (See Appendices B-D)

  • G. Hamarneh, Licentiate Thesis Deformable Spatio-Temporal Shape Modeling. Chalmers University of Technology, 1999. Technical report 311L. (See Section 4.3)

Medial Shape Profiles

Matlab GUI demo for shape representation & (statistics- and operator-based) deformations 

Synthetic Sequence Generator

Matlab GUI for generating synthetic sequences (shapes and images)