# Explainable Liver Segmentation and Volume Assessment Using Parallel Cropping
## 2 Background and Motivation  
In medical image analysis, particularly for liver segmentation, input data often contains substantial irrelevant information that does not contribute to the target RoI. Processing these unnecessary voxels increases memory usage, causes longer computation times, and reduces overall system responsiveness. Traditional slice-based cropping approaches involve multiple kernel invocations and memory transfers between CPU and GPU. Removing unnecessary data reduces memory usage, avoiding redundant memory transfers and unnecessary computations and, thus, reducing execution time [9; 30]. Cropping helps select the desired RoI of the targeted application in the analysis of medical imaging data. Due to poor image quality, the object's boundary in RoI is sometimes diffused in a CT liver image, making it challenging to find the RoI automatically [31]. Previous works such as [9; 30] have illustrated the computational drawbacks of these multi-pass approaches. We propose a GPU-based voxel-level cropping technique integrated into the segmentation pipeline to discard irrelevant data in a single pass, dramatically reducing kernel calls and intermediate memory operations. Figure 1: Proposed pipeline integrating voxel-level parallel cropping with Chan-Vese and U-Net liver segmentation, followed by volume assessment. From a clinical standpoint, accelerating liver segmentation pipelines is essential for time-sensitive workflows such as preoperative planning or intraoperative navigation [7]. In hospitals with constrained computational resources, the proposed cropping method enables the deployment of segmentation models with smaller memory footprints and faster execution, facilitating broader accessibility and practical integration into daily radiological operations [23; 31]. [PERSON] et al. [32] proposed the creation of a robust segmentation model that maintains high accuracy even when incomplete overlap between liver lesions in different imaging phases exists. [PERSON] et al. [33] proposed a novel Transformer-CNN combined network using multi-scale feature learning for three-dimensional (3D) medical image segmentation. [PERSON] et al. [30; 34] have discussed parallel cropping for image segmentation. [PERSON] et al. explored U-Net derived from a traditional U-Net for the liver and liver-tumor segmentation. Their method utilized dilated convolution and dense structure [35]. In [36], [PERSON] et al. used artificial intelligence based on the k-means clustering method for liver segmentation. Their model shows improved performance when compared with the region-growing method. [PERSON] et al. [37] proposed Attention U-Net++ (a nested attention-aware segmentation network) with deep supervised encoder-decoder architecture and a redesigned dense skip connection. [PERSON] et al. [38] presented a multi-scale attention net combing self-attention algorithm that adaptively integrates local features with global dependencies. [PERSON] et al. [39] developed a stratified squamous epithelium biopsy image classifier-based sequential computing environment for image segmentation. Finally, [PERSON] et al. [23] provided an overview of the available deep learning approaches for segmenting the liver and detecting liver tumors and their evaluation metrics, including accuracy, volume overlap error, dice coefficient, and mean square distance. Most recently, some DL implementations have been developed for liver and tumor segmentation. Ref. [40] proposes a deep learning architecture based on the Swin-Transformer structure, which is employed to capture the spatial relationships and global information for liver and tumor segmentation. The DRDA-Net framework [41] analyzes CT liver images, integrating deep residual learning and dual-attention mechanisms to extract local and global contextual features. Finally, ref. [42] presents a hybrid ResUNet model combining the ResNet and U-Net models. They crop the medical image dataset before processing. The CPU allocates the memory equivalent to the cropped size to copy data to the cropped image on the GPU.