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Insights into Imaging
Fig. 4 | Insights into Imaging

Fig. 4

From: Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI

Fig. 4

Demonstration of predicted tumor contours in a patient with endometrial cancer using various training combinations and sample sizes on the uterine dataset. A The tumor contour was delineated manually (red contour) and overlaid on the ADC image. The blue contours delineate the automatically generated tumor regions by using: B pretrained cervical model; C uterine-only model; D TL model with fine-tuned at L1 level. The numbers in white at the right bottom of each image indicate the DSC of the case. The pretrained cervical model itself generated only a small part of the tumor with DSC = 0.18. The accuracy increased as more uterine data were added for fine-tuning. The TL model outperformed the uterine-only model when the fine-tuned data size was < 128. The uterine-only model exhibited the highest DSC of 0.92 when all patient data were used (n = 256)

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