In general, deep learning based models require a tremendous amount of samples for appropriate training, which is difficult to satisfy in the medical field. This issue can usually be avoided with a proper initialization of the weights. On the task of medical image segmentation in general, two techniques are usually employed to tackle the training of a deep network @math . The first one consists in reusing some weights of a network @math pre-trained on a large scale database ( @math ImageNet). This procedure, also known as , happens to reduce the flexibility when it comes to new network design since @math is constrained to match some parts of @math . The second commonly used technique consists in working on image patches to benefit from the large number of available patches. This paper brings together these two techniques and propose to train arbitrarily designed networks, with a focus on relatively small databases, in two stages: patch pre-training and full sized image fine-tuning. An experimental work have been carried out on the tasks of retinal blood vessel segmentation and the optic disc one, using four publicly available databases. Furthermore, three types of network are considered, going from a very light weighted network to a densely connected one. The final results show the efficiency of the proposed framework along with state of the art results on all the databases.
A proof of concept is presented in our previous work @cite_31 . Promising results have been obtained when using the patch network's weights as an initialization point of the image network. However the patch network's metrics tend to be better than the image ones. This remark may question the importance of the transfer. Moreover, only one database and a single network have been considered. Hence, one may question the generalization to other databases and networks.
@cite_31
2890916832
Fully convolutional networks (FCNs) are well known to provide state-of-the-art results in various medical image segmentation tasks. However, these models usually need a tremendous number of training samples to achieve good performances. Unfortunately, this requirement is often difficult to satisfy in the medical imaging field, due to the scarcity of labeled images. As a consequence, the common tricks for FCNs’ training go from data augmentation and transfer learning to patch-based segmentation. In the latter, the segmentation of an image involves patch extraction, patch segmentation, then patch aggregation. This paper presents a framework that takes advantage of all these tricks by starting with a patch-level segmentation which is then extended to the image level by transfer learning. The proposed framework follows two main steps. Given a image database ( D ), a first network ( N _P ) is designed and trained using patches extracted from ( D ). Then, ( N _P ) is used to pre-train a FCN ( N _I ) to be trained on the full sized images of ( D ). Experimental results are presented on the task of retinal blood vessel segmentation using the well known publicly available DRIVE database.