Data Painter: A Tool for Colormap Interaction

The choice of a mapping from data to color should involve careful consideration in order to maximize the user understanding of the underlying data. It is desirable for features within the data to be visually separable and identifiable. Current practice involves selecting a mapping from predefined colormaps or coding specific colormaps using software such as MATLAB. The purposes of this paper are to introduce interactive operations for colormaps that enable users to create more visually distinguishable pixel based visualizations, and to describe our tool, Data Painter, that provides a fast, easy to use framework for defining these color mappings. We demonstrate the use of the tool to create colormaps for various application areas and compare to existing color mapping methods. We present a new objective measure to evaluate their efficacy

Sampling strategies for learning-based 3D medical image compression

Recent achievements of sequence prediction models in numerous domains, including compression, provide great potential for novel learning-based codecs. In such models, the input sequence’s shape and size play a crucial role in learning the mapping function of the data distribution to the target output. This work examines numerous input configurations and sampling schemes for a many-to-one sequence prediction model, specifically for compressing 3D medical images (16-bit depth) losslessly. The main objective is to determine the optimal practice for enabling the proposed Long Short-Term Memory (LSTM) model to achieve high compression ratio and fast encoding-decoding performance.Our LSTM models are trained with 4-fold cross-validation on 12 high-resolution CT dataset while measuring model’s compression ratios and execution time. Several configurations of sequences have been evaluated, and our results demonstrate that pyramid-shaped sampling represents the best trade-off between performance and compression ratio (up to 3x). We solve a problem of non-deterministic environments that allow our models to run in parallel without much compression performance drop.Experimental evaluation was carried out on datasets acquired by different hospitals, representing different body segments, and distinct scanning modalities (CT and MRI). Our new methodology allows straightforward parallelisation that speeds-up the decoder by up to 37x compared to previous methods. Overall, the trained models demonstrate efficiency and generalisability for compressing 3D medical images losslessly while still outperforming well-known lossless methods by approximately 17% and 12%. To the best of our knowledge, this is the first study that focuses on voxel-wise predictions of volumetric medical imaging for lossless compression

MedZip: 3D medical images lossless compressor using recurrent neural network (LSTM)

As scanners produce higher-resolution and more densely sampled images, this raises the challenge of data storage, transmission and communication within healthcare systems. Since the quality of medical images plays a crucial role in diagnosis accuracy, medical imaging compression techniques are desired to reduce scan bitrate while guaranteeing lossless reconstruction. This paper presents a lossless compression method that integrates a Recurrent Neural Network (RNN) as a 3D sequence prediction model. The aim is to learn the long dependencies of the voxel's neighbourhood in 3D using Long Short-Term Memory (LSTM) network then compress the residual error using arithmetic coding. Experiential results reveal that our method obtains a higher compression ratio achieving 15% saving compared to the state-of-the-art lossless compression standards, including JPEG-LS, JPEG2000, JP3D, HEVC, and PPMd. Our evaluation demonstrates that the proposed method generalizes well to unseen modalities CT and MRI for the lossless compression scheme. To the best of our knowledge, this is the first lossless compression method that uses LSTM neural network for 16-bit volumetric medical image compression

Lossless compression for volumetric medical images using deep neural network with local sampling

Data compression forms a central role in handling the bottleneck of data storage, transmission and processing. Lossless compression requires reducing the file size whilst maintaining bit-perfect decompression, which is the main target in medical applications. This paper presents a novel lossless compression method for 16-bit medical imaging volumes. The aim is to train a neural network (NN) as a 3D data predictor, which minimizes the differences with the original data values and to compress those residuals using arithmetic coding. We evaluate the compression performance of our proposed models to state-of-the-art lossless compression methods, which shows that our approach accomplishes a higher compression ratio in comparison to JPEG-LS, JPEG2000, JP3D, and HEVC and generalizes well