## 3 Results
### Deep Learning Models
#### 3.3.1 U-Net and Derived Models
The method is based on the use of three specific time points to capture the contrast agent's kinetic information, specifically the pre-contrast image, the post-contrast image after 2 min, and the post-contrast image after 6 min. Their model works on 2D slices and requires several preprocessing steps: the generation of a breast mask using a multiplanar 2D U-Net to remove misleading tissues and the application of motion correction. From the masked image, a three-channel image is created by stacking slices from the three different time points. Additionally, the left and right breasts are separated for the training procedure. A further difference from the original U-Net is the use of a Dice loss function. The model was trained and tested using 2D slices from a set of 33 subjects. Performance was estimated through 10-fold cross-validation (CV), obtaining a median Dice score of about 0.70, considering both lesion and healthy slices. Moreover, the three-time-point framework was compared with the basic U-Net and U-Net++, achieving higher performance compared to the original models, which had median Dice scores of about 0.67 and 0.65, respectively. Another approach for the segmentation of malignant lesions using multiple time points of the DCE-MRI series was presented by [PERSON] et al. [38]. Starting from the idea of using the entire sequence information, they proposed an ensemble of three U-Nets, each using different time points as input channels. They modified the basic U-Net architecture to handle 3D data by replacing standard convolutional blocks with residual blocks while maintaining the usual BCE loss. Several preprocessing steps were necessary to train the network, including the generation of a breast ROI using landmark detection to remove other enhanced organs, normalisation, and balanced extraction of smaller 3D patches from the ROI. The performance of the ensemble was assessed using 5-fold CV on a dataset with 46 cases, comparing automatic segmentation with radiologists' ground truth. The final segmentations were obtained through both majority voting and union of the three U-Nets, achieving mean Dice scores of 0.679 \(\pm\) 0.258 and 0.674 \(\pm\) 0.224, respectively. However, some false positives corresponding to axillary lymphadenopathy and vessels were reported. When considering only the main lesion, mean Dice scores of 0.728 \(\pm\) 0.243 and 0.790 \(\pm\) 0.172 were achieved, respectively. They also observed that maintaining the original image resolution yielded better results compared to setting a forced isotropic voxel size. The U-Net ensemble was further compared to FCM segmentations, consistently showing better results on the same dataset. Thus, the study suggests the possibility of using fully automatic methods that do not require user selection of the ROI. An alternative multiple time point ensemble solution was proposed in the study by [PERSON] et al. [39], which focuses on the use of 3D U-Net to segment malignant breast tumours in DCE-MRI. In this case, three separate 3D U-Nets were trained using the following inputs: only the first post-contrast image, the first post-contrast image and the first subtracted image with a fusion strategy at the image level, and the first post-contrast image and the first subtracted image with a fusion strategy at the feature level. A weighted combined loss was used, consisting of a BCE and a Dice term. Preprocessing steps were required, including correction for bias field gain, resampling, cropping to a fixed-size bounding box to cover the whole breast area, and intensity normalisation. The model was developed on a dataset of 141 cases, with 111 cases used for training and 30 for testing. The data were collected from various institutes using diverse scanners. Ground truth was provided by two experienced radiologists, with an inter-operator variability in terms of Dice score of 0.78 \(\pm\) 0.10. The performances of the individual components of the ensemble reached mean Dice scores of 0.73 \(\pm\) 0.23, 0.75 \(\pm\) 0.20, and 0.70 \(\pm\) 0.26, respectively.