# Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
## 2 Background and Related Works
### Related Works  
In [13], the authors present a deep learning based segmentation model that relies on image-specific fine tuning. The presented model performs bounding based binary segmentation with a P-Net [14], a structure adapted from VGG-16 [15], a well known CNN model. The proposed model shows good performance, but it requires an extended number of training iterations and samples to reach good performance. Ref. [16] makes use of a modified U-Net network [4] for Image Segmentation. After an initial phase of image augmentation, the proposed network shows a good performance, but it is focused only on liver images. Other medical images with more sparse binary masks are not considered. An interesting analysis of the impact of the different parts of the U-Net architecture on segmentation accuracy is presented in [17]. In such a paper, the authors propose a reduced version of the U-Net network that sensibly reduces the number of operations required for segmentation. Nevertheless, this model does not always reach the same performance results of the standard U-Net network. Ref. [18] presents an exhaustive survey of the state-of-the-art U-Net-based Image Segmentation, with its numerous application fields. In [18], the need for improvement techniques without relying on extended datasets is mentioned as a research challenge. For what concerns the segmentation of images with a sparse target signal, ref. [19] adds the attention mechanism ECA-Net (Efficient Channel Attention Neural Networks) into the standard U-Net architecture in order to improve the ability of the model to segment small items in the target images. This approach was applied on insulator string images and was not tested for biomedical Image Segmentation. In [20], the performance of U-Net for COVID-19 lesion segmentation on lung CT-Scans is compared with the achievable performance of another deep learning model, SegNet. Although SegNet produces better lesion detection accuracy, U-Net turns out to be better for multi-class segmentation. A  
Figure 1: Model of the U-Net architecture used in this work. Each blue rectangle represents a multi-channel feature map. The number of channels is written above each box and represents the value used in the experiments. The white boxes represent the copied feature maps. The arrows denote the type of operation performed. solution for segmentation tasks dealing with organs of highly varying dimensions is proposed in [21]. The authors consider segmentation in head and neck (HaN) CT images, which is characterized by the presence of big and small organs. The proposed solution consists of combining a standard deep learning network for 3D images, the S-net, with a smaller one specialized for smaller segments. The main network provides the secondary one with the probabilistic location of the small organs in the HaN samples. This ensemble, called FocusNet, produced good performance using the publicly available MICCAI 2015 Head and Neck dataset. Nevertheless, it requires the introduction of a new module, which increases the model complexity and the training effort. In this regard, a lightweight solution based on data pre-processing is proposed. Similarly, ref. [22] shows a solution for small organ segmentation, considering whole-body Magnetic Resonance Imaging (MRI) scans. With the implementation of a two-staged fully CNN, a coarse-scale segmentation is first executed and then refined in order to refine the segmentation of the considered organs. Although the proposed method has a 50% gain with respect to the the state-of-the-art for small organ MRI segmentation, the overall performance is still unsatisfactory, with a 0.56 Dice similarity coefficient. Finally, blood vessel segmentation is investigated in [23; 24]. In [23], a U-Net is used to perform coronary artery stenosis detection on X-ray coronary angiograms, including a module that leverages the temporal consistency of consecutive frames to limit the number of false positives. Ref. [24] is focused on the segmentation of coronary angiograms.