4 research outputs found
Solid Spherical Energy (SSE) CNNs for Efficient 3D Medical Image Analysis
Invariance to local rotation, to differentiate from the global rotation of images and objects, is required in various texture analysis problems. It has led to several breakthrough methods such as local binary patterns, maximum response and steerable filterbanks. In particular, textures in medical images often exhibit local structures at arbitrary orientations. Locally Rotation Invariant (LRI) Convolutional Neural Networks (CNN) were recently proposed using 3D steerable filters to combine LRI with Directional Sensitivity (DS). The steerability avoids the expensive cost of convolutions with rotated kernels and comes with a parametric representation that results in a drastic reduction of the number of trainable parameters. Yet, the potential bottleneck (memory and computation) of this approach lies in the necessity to recombine responses for a set of predefined discretized orientations. In this paper, we propose to calculate invariants from the responses to the set of spherical harmonics projected onto 3D kernels in the form of a lightweight Solid Spherical Energy (SSE) CNN. It offers a compromise between the high kernel specificity of the LRI-CNN and a low memory/operations requirement. The computational gain is evaluated on 3D synthetic and pulmonary nodule classification experiments. The performance of the proposed approach is compared with steerable LRI-CNNs and standard 3D CNNs, showing competitive results with the state of the art
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.Comment: 62 pages. For additional information see https://theibsi.github.io