Semi-supervised learning towards automated segmentation of PET images with limited annotations: Application to lymphoma patients
arxiv(2022)
摘要
The time-consuming task of manual segmentation challenges routine systematic
quantification of disease burden. Convolutional neural networks (CNNs) hold
significant promise to reliably identify locations and boundaries of tumors
from PET scans. We aimed to leverage the need for annotated data via
semi-supervised approaches, with application to PET images of diffuse large
B-cell lymphoma (DLBCL) and primary mediastinal large B-cell lymphoma (PMBCL).
We analyzed 18F-FDG PET images of 292 patients with PMBCL (n=104) and DLBCL
(n=188) (n=232 for training and validation, and n=60 for external testing). We
employed FCM and MS losses for training a 3D U-Net with different levels of
supervision: i) fully supervised methods with labeled FCM (LFCM) as well as
Unified focal and Dice loss functions, ii) unsupervised methods with Robust FCM
(RFCM) and Mumford-Shah (MS) loss functions, and iii) Semi-supervised methods
based on FCM (RFCM+LFCM), as well as MS loss in combination with supervised
Dice loss (MS+Dice). Unified loss function yielded higher Dice score (mean +/-
standard deviation (SD)) (0.73 +/- 0.03; 95
loss (p-value<0.01). Semi-supervised (RFCM+alpha*LFCM) with alpha=0.3 showed
the best performance, with a Dice score of 0.69 +/- 0.03 (95
outperforming (MS+alpha*Dice) for any supervision level (any alpha) (p<0.01).
The best performer among (MS+alpha*Dice) semi-supervised approaches with
alpha=0.2 showed a Dice score of 0.60 +/- 0.08 (95
another supervision level in this semi-supervised approach (p<0.01).
Semi-supervised learning via FCM loss (RFCM+alpha*LFCM) showed improved
performance compared to supervised approaches. Considering the time-consuming
nature of expert manual delineations and intra-observer variabilities,
semi-supervised approaches have significant potential for automated
segmentation workflows.
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