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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

05.10.2023 | Original Article

MC3DU-Net: a multisequence cascaded pipeline for the detection and segmentation of pancreatic cysts in MRI

verfasst von: Nir Mazor, Gili Dar, Richard Lederman, Naama Lev-Cohain, Jacob Sosna, Leo Joskowicz

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 3/2024

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Abstract

Purpose

Radiological detection and follow-up of pancreatic cysts in multisequence MRI studies are required to assess the likelihood of their malignancy and to determine their treatment. The evaluation requires expertise and has not been automated. This paper presents MC3DU-Net, a novel multisequence cascaded pipeline for the detection and segmentation of pancreatic cysts in MRI studies consisting of coronal MRCP and axial TSE MRI sequences.

Methods

MC3DU-Net leverages the information in both sequences by computing a pancreas Region of Interest (ROI) segmentation in the TSE MRI scan, transferring it to MRCP scan, and then detecting and segmenting the cysts in the ROI of the MRCP scan. Both the voxel-level ROI of the pancreas and the segmentation of the cysts are performed with 3D U-Nets trained with Hard Negative Patch Mining, a new technique for class imbalance correction and for the reduction in false positives.

Results

MC3DU-Net was evaluated on a dataset of 158 MRI patient studies with a training/validation/testing split of 118/17/23. Ground truth segmentations of a total of 840 cysts were manually obtained by expert clinicians. MC3DU-Net achieves a mean recall of 0.80 ± 0.19, a mean precision of 0.75 ± 0.26, a mean Dice score of 0.80 ± 0.19 and a mean ASSD of 0.60 ± 0.53 for pancreatic cysts of diameter > 5 mm, which is the clinically relevant endpoint.

Conclusion

MC3DU-Net is the first fully automatic method for detection and segmentation of pancreatic cysts in MRI. Automatic detection and segmentation of pancreatic cysts in MRI can be performed accurately and reliably. It may provide a method for precise disease evaluation and may serve as a second expert reader.

Vorheriger Artikel An ex-vivo and in-vitro dynamic simulator for surgical and transcatheter mitral valve interventions

Nächster Artikel Improved segmentation of basal ganglia from MR images using convolutional neural network with crossover-typed skip connection

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Titel: MC3DU-Net: a multisequence cascaded pipeline for the detection and segmentation of pancreatic cysts in MRI
verfasst von: Nir Mazor
Gili Dar
Richard Lederman
Naama Lev-Cohain
Jacob Sosna
Leo Joskowicz
Publikationsdatum: 05.10.2023
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 3/2024
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-023-03020-y

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Abstract

Purpose

Methods

Results

Conclusion

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