## 1 Introduction  
Deep Learning Networks for Automatic Retroperitoneal  
Sarcoma Segmentation in Computerized Tomography  
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## 1 Introduction  
Soft tissue sarcomas are rare, malignant mesenchymal neoplasms that account for less than 1% of all malignant tumors. Of all sarcomas, the majority occur outside of the retroperitoneum, while around 10% of all sarcomas occur in the retroperitoneum [1], with a mean annual incidence of 2.7 per million [2]. The prognosis for patients with retroperitoneal sarcoma (RPS) is relatively poor, with a 36% to 58% overall 5-year survival rate and a natural history characterized by late recurrence [3]. RPS are frequently underdiagnosed at the early stage, and symptoms appear late, as they are associated with the displacement of adjacent organs and obstructive phenomena. When present, symptoms include abdominal pain, back pain, bowel obstruction, or palpable abdominal mass [1]. A variety of imaging techniques, including computed tomography (CT), positron emission tomography-computed tomography (PET/CT), and magnetic resonance imaging (MRI), may be used to assess RPS. Among them, CT is the most commonly used modality for the identification, localization, and staging of RPS [4]. CT examination allows for tissue components characterization and offers multiplanar reconstructions to easily depict the anatomic site of the origin of a mass, as well as its relationship to adjacent organs and vasculature [5]. RPS is one of the largest tumors of the human body [6]: lesions with a measure of \(<\)5 cm are considered rare, while a measure of \(>\)20 cm is found in 20 to 50% of masses at the time of resection [7]. Despite RPS's large dimension, the impact of tumor size on the patient's survival remains controversial. Several previous studies have failed to demonstrate any association of tumor size [8; 9; 10; 11; 12; 13; 14; 15; 16], while others have found that a size threshold of 10 cm is significant for survival [17; 18]. However, these previous studies analyzed just the largest lesion's diameter instead of the volume. RPS have an irregular shape, and the largest diameter cannot reflect the real tumor volume. Manual segmentation based on imaging sections could be used for a volume estimation, and it is considered the gold standard for segmentation methods; however it is time-consuming, requires experience, and is strongly operator-dependent. Recently, deep learning methods, especially supervised classification methods based on convolutional neural networks, have been successful in the field of medical imaging for segmenting the anatomy of interest [19; 20]. To our knowledge, no prior studies explored deep learning methods for RPS automatic segmentation.