Standardised convolutional filtering for radiomics

The Image Biomarker Standardisation Initiative (IBSI) aims to improve reproducibility of radiomics studies by standardising the computational process of extracting image biomarkers (features) from images. We have previously established reference values for 169 commonly used features, created a standard radiomics image processing scheme, and developed reporting guidelines for radiomic studies. However, several aspects are not standardised. Here we present a preliminary version of a reference manual on the use of convolutional image filters in radiomics. Filters, such as wavelets or Laplacian of Gaussian filters, play an important part in emphasising specific image characteristics such as edges and blobs. Features derived from filter response maps have been found to be poorly reproducible. This reference manual forms the basis of ongoing work on standardising convolutional filters in radiomics, and will be updated as this work progresses.

[1]  M. Hatt,et al.  Joint EANM/SNMMI guideline on radiomics in nuclear medicine , 2022, European Journal of Nuclear Medicine and Molecular Imaging.

[2]  Abhinav K. Jha,et al.  Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines) , 2022, The Journal of Nuclear Medicine.

[3]  Vincent Andrearczyk,et al.  Local Rotation Invariance in 3D CNNs , 2020, Medical Image Anal..

[4]  R. Steenbakkers,et al.  The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. , 2020, Radiology.

[5]  J. Mongan,et al.  Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. , 2020, Radiology. Artificial intelligence.

[6]  Taco S Cohen,et al.  Pulmonary nodule detection in CT scans with equivariant CNNs , 2019, Medical Image Anal..

[7]  Alex Zwanenburg,et al.  Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis , 2019, European Journal of Nuclear Medicine and Molecular Imaging.

[8]  Aaron O'Leary,et al.  PyWavelets: A Python package for wavelet analysis , 2019, J. Open Source Softw..

[9]  Vincent Andrearczyk,et al.  Exploring local rotation invariance in 3D CNNs with steerable filters , 2018, MIDL.

[10]  Hania Al-Hallaq,et al.  Variation in algorithm implementation across radiomics software , 2018, Journal of medical imaging.

[11]  Michael Unser,et al.  Rotation Invariance and Directional Sensitivity: Spherical Harmonics versus Radiomics Features , 2018, MLMI@MICCAI.

[12]  R. Sarpong,et al.  Bio-inspired synthesis of xishacorenes A, B, and C, and a new congener from fuscol† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc02572c , 2019, Chemical science.

[13]  Daniel L. Rubin,et al.  Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images , 2018, Journal of Digital Imaging.

[14]  Max Welling,et al.  3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data , 2018, NeurIPS.

[15]  Irène Buvat,et al.  LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity. , 2018, Cancer research.

[16]  Harini Veeraraghavan,et al.  Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research. , 2018, Medical physics.

[17]  Maurice Weiler,et al.  Learning Steerable Filters for Rotation Equivariant CNNs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[18]  Ender Konukoglu,et al.  Post-radiochemotherapy PET radiomics in head and neck cancer - The influence of radiomics implementation on the reproducibility of local control tumor models. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[19]  Andriy Fedorov,et al.  Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.

[20]  P. Lambin,et al.  Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.

[21]  Michael Unser,et al.  Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification , 2017, IEEE Transactions on Image Processing.

[22]  Henning Müller,et al.  3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms , 2017, IEEE Transactions on Image Processing.

[23]  Steffen Löck,et al.  Image biomarker standardisation initiative , 2016 .

[24]  Pablo Hernandez-Cerdan,et al.  Isotropic and Steerable Wavelets in N Dimensions. A multiresolution analysis framework for ITK , 2016, The Insight Journal.

[25]  Max Welling,et al.  Group Equivariant Convolutional Networks , 2016, ICML.

[26]  Jinzhong Yang,et al.  IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. , 2015, Medical physics.

[27]  Gary S Collins,et al.  Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement , 2015, BMC Medicine.

[28]  Prateek Prasanna,et al.  Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): Distinguishing Tumor Confounders and Molecular Subtypes on MRI , 2014, MICCAI.

[29]  Xiaoou Tang,et al.  Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.

[30]  Hung-Ming Wang,et al.  Development and Evaluation of an Open-Source Software Package “CGITA” for Quantifying Tumor Heterogeneity with Molecular Images , 2014, BioMed research international.

[31]  Samuel H. Hawkins,et al.  Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. , 2014, Translational oncology.

[32]  Stephen M. Moore,et al.  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.

[33]  Michael Unser,et al.  3D Steerable Wavelets in Practice , 2012, IEEE Transactions on Image Processing.

[34]  Dimitri Van De Ville,et al.  Steerable pyramids and tight wavelet frames in L 2 ( R d ) , 2011 .

[35]  Ramakrishna Kakarala,et al.  A theory of phase-sensitive rotation invariance with spherical harmonic and moment-based representations , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[36]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[37]  Dimitri Van De Ville,et al.  Wavelet Steerability and the Higher-Order Riesz Transform , 2010, IEEE Transactions on Image Processing.

[38]  Francesco Bianconi,et al.  Evaluation of the effects of Gabor filter parameters on texture classification , 2007, Pattern Recognit..

[39]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[40]  Eero P. Simoncelli,et al.  A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients , 2000, International Journal of Computer Vision.

[41]  Nicolai Petkov,et al.  Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells , 1997, Biological Cybernetics.

[42]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[43]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[44]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[45]  Kenneth I. Laws,et al.  Rapid Texture Identification , 1980, Optics & Photonics.

[46]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[47]  Julien Fageot,et al.  Fundamentals of Texture Processing for Biomedical Image Analysis: A General Definition and Problem Formulation , 2017 .

[48]  Artur Klepaczko,et al.  MaZda – A Framework for Biomedical Image Texture Analysis and Data Exploration , 2017 .

[49]  Stéphane Mallat,et al.  Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities , 2017, NIPS.

[50]  Roger Schaer,et al.  QuantImage: An Online Tool for High-Throughput 3D Radiomics Feature Extraction in PET-CT , 2017 .

[51]  Adrien Depeursinge,et al.  Biomedical Texture Operators and Aggregation Functions: A Methodological Review and User's Guide , 2017 .

[52]  Dimitris Visvikis,et al.  Characterization of PET/CT images using texture analysis: the past, the present… any future? , 2016, European Journal of Nuclear Medicine and Molecular Imaging.

[53]  El Naqa,et al.  A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities , 2015 .

[54]  Dimitris N. Metaxas,et al.  Extraction and Tracking of MRI Tagging Sheets Using a 3D Gabor Filter Bank , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[55]  P. Dutilleux.  An Implementation of the “algorithme à trous” to Compute the Wavelet Transform , 1989 .