## 2 Literature Review  
MRI is the most cost-efficient [5] and sensitive modality for liver tumor detection [6; 21], though it is challenging from the perspective of automatic segmentation methods. One challenge with MRI is a variable contrast between liver and tumors depending on the sequence and the time passed after contrast injection. Any machine learning-based method requires expert annotated data for the desired task. That is why another challenge with MRI is the lack of publicly available annotated data suited to train automatic segmentation methods. Here, we used the in-house COMET dataset [22], which contains various source input data, such as machines and protocols within the T1 contrast-enhanced MRI sequence. The use of machine learning methods for image segmentation has seen rapid growth in the past decade. In 2019 [PERSON] et al. released an open dataset of 131 CT cases with segmented liver and tumor segmentation [23]. The number of research papers on CT-based DL in liver and tumor segmentation has been growing [13; 24]. In contrast, MRI-based DL segmentation remains a challenge due to the lack of data availability and the demand for ground truth. Several papers approached this problem using a private MRI dataset and different DL solutions [25]. The most common DL methods to segment liver and liver tumors are based on the FCNN networks [13]. The choice between 2D and 3D convolutional filters depends on the specifics of the task and on the computational resources available. In the current study, volumetric 3D MRI data were used where lesions extend across multiple slices in the 3D volume, and a 3D model approach was therefore chosen; 3D convolution is an extension of a standard 2D convolution [26] into a third dimension. The benefit of using the third dimension is utilizing 3D spatial information from MRI volumes. For example, the vessel representation in a single 2D slice could resemble a lesion; however, with 3D information, the difference between those structures may become more evident. Furthermore, 3D convolutions solve the discontinuity problem across slices of the 3D image volume [27]. Despite an increasing number of 3D U-net variations [18; 28; 29; 30], the original 3D U-net [31] has the lowest number of parameters and shows good segmentation results on most of the medical segmentation tasks [32]. Despite the higher memory consumption, V-net [18] and SegResNet [19] show promising results in MRI segmentation tasks for brain tumor segmentation tasks. HighResNet is another 3D high-resolution convolutional network designed for volumetric image segmentation [20; 33; 34]. The use of dilated convolutions has already shown high-accuracy results in tumor detection on MRI brain images [35]. Compared to encode-decode networks, HighResNet has fewer training parameters (809K parameters compared to 4.8 M for 3D U-net). The cascade approach showed promising results for tumor detection, as it helps to eliminate the background by finding the bounding box of the liver on both CT and MRI sequences [29; 36; 37; 38; 39]. Using an in-house dataset of diffusion-weighted MRI, [PERSON] et al. segmented HCC tumors using a cascade U-net with a mean Dice score of 0.870 for liver and 0.697 for tumors [29; 36; 37; 38; 39]. Our study employed a cascade method based on four different FCNN networks to segment liver and CLRM tumors from MRI images. In contrast with the other methods, we proposed to use the network from the first stage as a weight initializer for a second network which is explained in detail in the Section 3.2.2. We aimed to reduce the training time of the second network by introducing the MRI features from one network to another. Studies have shown that a higher Dice score for the tumor segmentation could be achieved mainly by using multiple sequences and/or contrast phases from MRI examination. For example, a Dice score of 0.91 for the liver and 0.68 for the tumor segmentation was achieved for HCC tumors using 3 T1 weighted MRI from different post contrast phases [40].