Assessing the role of random forests in medical image segmentation

  • Neural networks represent a field of research that can quickly achieve very good results in the field of medical image segmentation using a GPU. A possible way to achieve good results without GPUs are random forests. For this purpose, two random forest approaches were compared with a state-of-the-art deep convolutional neural network. To make the comparison the PhC-C2DH-U373 and the retinal imaging datasets were used. The evaluation showed that the deep convolutional neutral network achieved the best results. However, one of the random forest approaches also achieved a similar high performance. Our results indicate that random forest approaches are a good alternative to deep convolutional neural networks and, thus, allow the usage of medical image segmentation without a GPU.

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Author:Dennis Hartmann, Dominik MüllerORCiDGND, Iñaki Soto-ReyORCiD, Frank KramerORCiDGND
Frontdoor URL
Parent Title (English):arXiv
Year of first Publication:2021
Release Date:2023/06/12
First Page:arXiv:2103.16492
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Medizinische Fakultät
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für IT-Infrastrukturen für die Translationale Medizinische Forschung
Medizinische Fakultät / Universitätsklinikum
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