7.2 Future Work
7.2.2 Lesion Inpainting Independent of Segmentation
The current workaround for performing a more accurate morphological analysis for images from MS patients is using the lesion inpainting techniques with the manual lesion masks. However, this is challenging to obtain accurate lesion segmentation. Even I pro- posed an inpainting algorithm in Chapter 4 which is robust to lesion segmentation error, a fully automated lesion inpainting independent of lesion segmentation is desired. I hypoth- esize that, by reducing the dependence on segmentation, the inpainted images can provide more accurate quantification of cortical thickness. Currently, there is no existing approach to perform this.
One of the possible solution is the image translation, e.g. CycleGAN [131] and Pix2Pix [64]. Since I don’t have “ground-truth” pairs of lesion/lesion-free images, CycleGAN is a suitable model for this scenario. While the lesion areas may be small comparing to other regions, I can use patch-wise translation between patches. If the patch is relatively small so that some of them from the MS patients only contain NABT, I may also need labels from the segmentation to guide if theses patches have WM / GM lesions inside the patch. But these labels can be simple binary labels (True for lesion exist, False for NABT only) and are not required to be very accurate. On contrast to the unpaired approach, I can use Pix2Pix for developing pair-wise translation of images. To create the pair of lesion/non-lesion images, one intuitive idea is that, with one image from an MS patient as image A, I can inpaint the lesions using some existing inpainting techniques to generate image B. The image A and B can be a pair for training. However, the current inpainting techniques are not satisfactory and it is difficult to outperform those methods while using the labels from them. Instead, I can simulate the lesions from healthy controls. By using the simulated/original pairs of the image, the deep CNNs learn to translate from images with lesions to images without lesions. Nevertheless, there is a gap between the simulated dataset and the real-lesion dataset. Since the gap is not as large as between unrelated datasets, I assume that it can be
filled by domain adaptation techniques, for example, the data augmentations as I proposed in Chapter 5.
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