![]() ![]() Fig.1:Pipeline Schematic RepresentationFig. Given in input raw CRM the pipeline is able to (i) remove unuseful data, (ii) extract heart ROIs storing also Dicom metadata, (iii) normalize slices and image resolution, and (iv) store the processed CRM into ready-format for ML techniques. Conclusions: In this work, we presented a post-processing pipeline for CRM images and LGE analysis. In our series, the original dataset extended for about 200 GB by requesting 10 slices per subject with a resolution of 128 by 128 pixels (also extracting heart ROI) the final dimension was reduced to 108 MB. By using this pipeline, a great amount of information not needed for LGE analysis can be discarded, granting a significant reduction in terms of data storage. At the end of the ML pipeline, images can be reduced to a common resolution and forwarded to ML algorithms. weight, age, …) were stored using the json standard. To manage the data more easily, images were saved as a NumPy Array, while other useful Dicom metadata (e.g. This step allowed us to remove the background by only selecting the relevant ROIs. 2) then the images were cropped by keeping the largest bounding box. The network was applied on all the slices (Fig. In order to have a focus on the heart, a Region of Interest (ROI) extractor was implemented, based on a YOLO network for object detection. Given a desired final number of slices and resolution, the 3D-array was reshaped through a spline interpolation. ![]() The attributes were not homogeneous between subjects. Then, for each subject, the extracted series consists of a 3D-array (N,H,W), with N number of slices, and (H,W) image resolution. Some photographers will need a powerful organizing tool like Lightroom Classic (opens in new tab), some need one-click creative effects and inspiration, while others need the kind of technical quality that only the best RAW image processing tools (DxO PhotoLab 6 (opens in new tab)) can provide. Finally, DICOMs were grouped by image shape (demanding a min number of elements), and only the series with the highest resolution was retained. The best photo editing software for you isn't always the most expensive or complicated option. Scans were grouped together by image orientation (requesting a min and max number of elements per group) and only the group with the largest number of files was selected. Orientation of the major axis was computed and ‘Axial’ or ‘Coronal’ images removed. By looking at the metadata in raw files, ‘SequenceName’ tag was used to discard cine images, ‘ScanningSequence’ tag to select Gradient Recall and Inversion Recovery techniques (Inversion Time > 100 ms), ‘SequenceVariant’ tag to discard Steady State images (See Fig. Methods and Results: 642 consecutive CMR studies were analyzed. Additionally, steps for normalization of image number and automatically heart localization are outlined. Purpose: A ML pipeline for extraction of LGE images from raw DICOM data is presented. A common issue in data extraction is represented by noisy datasets, like those of CMR studies, characterized by multiple images, acquired by different techniques, axis orientation and contrast timing. Machine Learning (ML) pipelines are created for data of interest extraction and algorithm application. Background: Artificial Intelligence is an emergent tool in clinical practice for post processing of medical images. Abstract: Funding Acknowledgements: Type of funding sources: None. ![]()
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