## 1 Introduction  
A method that can obtain liver and liver tumor segmentation from Magnetic Resonance Imaging (MRI) images in just a few seconds can benefit doctors and patients while reducing the time needed for treatment planning. Colorectal liver metastases (CRLM) develop in approximately half of patients with colorectal cancer [1], causing the second-highest number of cancer-related deaths worldwide. In 2020 it was estimated that there were more than 1.9 million new cases of colorectal metastases worldwide [2], with more than 1700 cases registered in Norway [3]. Magnetic resonance imaging (MRI) is the most sensitive method for the detection of liver metastases [4; 5; 6]. Such patient-specific 3D models are also utilized for 3D printing or 3D visualization using virtual or augmented reality [7].  
One of the main limitations for using 3D models is the time required for annotation and segmentation from MRI scans [8; 9; 10]. Traditionally, segmentation is performed semi-automatically using tools such as a 3D-Slicer [11] or an ITK-snap [12]. The use of automatic methods can significantly decrease the annotation and 3D model acquiring time. Image segmentation is the most investigated area of deep learning (DL) application to medical images [13; 14], and DL-based methods are increasingly proving to outperform conventional automated segmentation methods [15]. The number of research papers about DL applied to MRI liver and tumor segmentation has constantly grown over the past five years [13]. Fully connected convolutional neural networks (FCNN) represent the current gold standard for feature extraction from complex two and three-dimensional medical data. U-net [16; 17], V-net [18], SegResNet [19] and HighResNet [20] are representations of such networks. While 3D U-net and V-net have already shown good literature results on liver and tumor segmentation tasks, SegResNet and HighResNet have not previously been applied to this task to the best of our knowledge.  
This study aimed to develop a DL-based tool for fast and accurate MRI-based volumetric segmentation of the whole liver and liver tumors. The highlights of this study are:  
* Detection and segmentation of liver metastasis from T1 MRI in less than 7 s.
* Creation of a cascade deep learning segmentation method based on the 3D FCNN.
* Comparison of four FCNN segmentation networks on the inhomogeneous MRI dataset.
* HighResNet application for the liver and liver lesion segmentation.
* Creation of a GUI to simplify the integration of the AI tool into medical practice.  
Utilizing an in-house MRI dataset, a cascade DL method based on FCNN was trained to segment CLRM and liver parenchyma from T1-weighted contrast-enhanced MRIs. In addition, this study evaluated the performance of the four most promising FCNNs within medical image segmentation on the validation set, which represents a highly unbalanced problem for segmentation with a limited number of training samples. Using the GUI, our method produced the segmentations for unannotated MRI data. Finally, we measured the time required to manually correct the obtained segmentation by our DL-based tool in order for the 3D model to be sufficient for further clinical use.