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Reinforcement learning-based anatomical maps for pancreas subregion and duct segmentationAmiri, Sepideh ...AbstractBackground: The pancreas is a complex abdominal organ with many anatom-ical variations, and therefore automated pancreas segmentation from medicalimages is a challenging application.Purpose: ... In this paper, we present a framework for segmenting individualpancreatic subregions and the pancreatic duct from three-dimensional (3D)computed tomography (CT) images.Methods: A multiagent reinforcement learning (RL) network was used to detectlandmarks of the head,neck,body,and tail of the pancreas,and landmarks alongthe pancreatic duct in a selected target CT image. Using the landmark detectionresults, an atlas of pancreases was nonrigidly registered to the target image,resulting in anatomical probability maps for the pancreatic subregions and duct.The probability maps were augmented with multilabel 3D U-Net architecturesto obtain the final segmentation results.Results: To evaluate the performance of our proposed framework, we com-puted the Dice similarity coefficient (DSC) between the predicted and groundtruth manual segmentations on a database of 82 CT images with manuallysegmented pancreatic subregions and 37 CT images with manually segmentedpancreatic ducts. For the four pancreatic subregions, the mean DSC improvedfrom 0.38, 0.44, and 0.39 with standard 3D U-Net, Attention U-Net, and shiftedwindowing (Swin) U-Net architectures, to 0.51, 0.47, and 0.49, respectively, whenutilizing the proposed RL-based framework. For the pancreatic duct, the RL-based framework achieved a mean DSC of 0.70, significantly outperformingthe standard approaches and existing methods on different datasets.Conclusions: The resulting accuracy of the proposed RL-based segmentationframework demonstrates an improvement against segmentation with standardU-Net architectures.Source: Medical physics. - ISSN 0094-2405 (Vol. , no. , 2024, 15 str.)Type of material - article, component part ; adult, seriousPublish date - 2024Language - englishCOBISS.SI-ID - 202626819
Author
Amiri, Sepideh |
Vrtovec, Tomaž |
Mustafaev, Tamerlan |
Deufel, Christopher L. |
Thomsen, Henrik S. |
Hylleholt Sillesen, Martin |
Gudmann Steuble Brandt, Erik |
Andersen, Michael Brun |
Müller, Christoph Felix |
Ibragimov, Bulat
Topics
analiza medicinskih slik |
razpoznavanje oslonilnih točk |
segmentacija slik |
trebušna slinavka |
globoko učenje |
spodbujevano učenje |
medical image analysis |
landmark detection |
image segmentation |
pancreas region |
deep learning |
reinforcement learning
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Author | Amiri, Sepideh ... |
Title | Reinforcement learning-based anatomical maps for pancreas subregion and duct segmentation |
Publication date | 2024-06-21 |
COBISS.SI-ID | 202626819 |
Publication version in repository | Publisher's version |
Publication licence | Creative Commons Attribution 4.0 International |
Embargo | Unidentified |
Project(s) from which the publication was funded
Title | Acronym | Project ID | Funder |
---|---|---|---|
Leveraging artificial intelligence for pancreatic cancer diagnosis, treatment planning and treatment outcome prediction | NFF20OC0062056 |
Novo Nordisk Foundation |
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Analiza biomedicinskih slik in signalov | P2-0232-2022 |
Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije |
|
Morfometrija medicinskih slik na podlagi globokega učenja za kardiovaskularne aplikacije | J2-50067-2023 |
Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije |
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Amiri, Sepideh | ![]() |
Vrtovec, Tomaž | 23404 |
Mustafaev, Tamerlan | ![]() |
Deufel, Christopher L. | ![]() |
Thomsen, Henrik S. | ![]() |
Hylleholt Sillesen, Martin | ![]() |
Gudmann Steuble Brandt, Erik | ![]() |
Andersen, Michael Brun | ![]() |
Müller, Christoph Felix | ![]() |
Ibragimov, Bulat | 33446 |
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