# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 1 Introduction
### Research Motivation  
In the field of medical image processing, deep learning has shown significant advantages. Deep learning techniques use image data to discover inherent patterns and conditionfeatures. In most cases, medical data interpretation are obtained through medical experts' analysis. Due to the subjective nature of medical imaging, doctors often make incorrect assessments [5]. Deep learning is an excellent way to improve the evaluation of medical images by traditional experts, and many scholars have started to incorporate deep learning techniques for medical imaging research. Deep learning image processing techniques are divided into image classification, target recognition, and semantic segmentation. Image classification techniques classify medical images for diseases, while target recognition techniques and semantic segmentation techniques perform lesion region detection. The existing deep learning semantic segmentation techniques are mainly applied to the human brain [6; 7; 8; 9], liver [10; 11; 12; 13], heart [14; 15; 16; 17], lung [18; 19; 20; 21], and kidney [22; 23; 24; 25] parts. The development of semantic segmentation techniques has been very rapid in recent years. In the early stage of technology development, people mainly used pixel-level cluster-based segmentation, threshold segmentation, and decision tree classification methods. Nowadays, researchers use Volumetric Memory Networks [26; 27; 28], Prototype View [29], Region Semantic Contrast and Aggregation [30], Deep Hierarchical Semantic Segmentation [31], and other methods for semantic segmentation research and application. Volumetric Memory Network is a neural network model for processing 3D data. While traditional neural networks mainly process flat image data, volumetric neural networks are specifically designed to process 3D data, such as stereo images, medical images, and 3D scan data. In [26], a novel memory-enhanced network VMN with rich and non-redundant structure, is proposed for volumetric interactive segmentation by combining interactive image segmentation methods with volumetric memory networks. A quality evaluation module is also proposed to facilitate the automatic suggestion of appropriate slices for iterative correction. The technique can handle various interactions, e.g., scribbles, bounding boxes, and extreme clicks. In [29], the prototype view approach is proposed, which identifies several limitations of the parametric isolation regime in semantic segmentation, followed by a non-parametric alternative based on unlearnable prototypes. Instead of learning individual weights/query vectors for each class in a fully parametric manner, the model represents each class as a set of unlearnable prototypes that depend only on the average features of several training pixels in that class. The work anticipates that this work will trigger a rethinking of the current de facto design of semantic segmentation models. In [30], learning semantic segmentation from weakly labeled data in the literature is challenging because inferring dense object regions from sparse semantic labels are complex. In that paper, Regional Semantic Comparison and Aggregation (RCA) is proposed, where semantic aggregation is used to collect various relational contexts in memory to enrich redundant semantic representations. In this way, RCA obtains robust fine-grained semantic understanding and eventually establishes new state-of-the-art results in PASCAL VOC 2012 and COCO 2014. Ref. [31] discusses hierarchical semantic segmentation (HSS), which aims at a structured pixel-level description of visual observations based on a class. Also designed is HSSN, a generalized HSSN directly converts HSS to a pixel-level multi-label classification task, bringing minimal architectural changes to the current segmentation model. HSSN first explores the inherent properties of the hierarchy as training targets, forcing segmentation predictions to obey the hierarchical structure.