Liver tumors, whether primary or metastatic, significantly impact cancer patients' outcomes. Accurate identification and precise quantification of liver cancer are crucial for effective patient management, including precise diagnosis, prognosis, and evaluation of anticancer therapy efficacy. The evaluation of liver tumor burden is crucial at different stages of cancer treatment, and it is typically performed on medical images, such as computed tomography (CT). Currently, this task is carried out manually by radiologists, which is not only time-consuming but also prone to variability between different observers and within the same one.

Furthermore, the need for a more comprehensive assessment of liver tumors extends to treatment monitoring in cancer patients. The evaluation of cancer volume changes throughout treatment on CT images, as opposed to relying solely on the maximum diameter of a few tumors (as defined by the standard Response Evaluation Criteria In Solid Tumors (RECIST) [1]), has the potential to offer a more accurate response assessment and prediction of clinical outcomes.

Here, we present SALSA (System for Automatic Liver tumor Segmentation And detection), a fully automated tool for liver tumor detection and delineation, implementing several Deep Learning (DL) methods for biomedical image segmentation.