# Explainable Liver Segmentation and Volume Assessment Using Parallel Cropping
Our work takes a complementary path by focusing on pre-processing techniques that can be integrated into such pipelines, reducing input complexity and accelerating inference. So, the most common liver dataset is LiTS [20], which provides only 131 livers. Recent DL-based proposals in prestigious journals have used LiTS. RMAU-Net [21] consists of a segmentation network incorporating residual and multi-scale attention mechanisms to enhance liver and tumor segmentation from CT scans. Ref. [22] explores a hybrid approach combining deep residual networks with a biplane segmentation method to optimize liver boundary detection and volume estimation. Both articles present high-performing segmentation methods. Their reliance on computational power and large datasets limits their applicability in real-time, resource-limited environments. DL models operate as _black boxes_. The decision-making process behind the segmentation results is not easily interpretable, making it difficult for clinicians to understand why the model produces certain outputs, especially in edge cases or when anomalies are present. In the medical field, transparency and the ability to explain outcomes are crucial for gaining trust and acceptance from healthcare professionals. The lack of interpretability in these DL models poses a barrier to clinical adoption, as practitioners might be hesitant to rely on a system that cannot provide clear justifications for its results. When a model performs poorly or produces incorrect segmentations, it is difficult to diagnose and address the cause of the error without a clear understanding of how the model processes the data. This lack of insight makes it challenging to fine-tune or adapt the model for different datasets or clinical environments, limiting its flexibility and robustness. In contrast, traditional methods provide transparency and robustness but suffer from slow execution, particularly when handling large volumetric medical images containing extensive irrelevant regions outside the organ of interest (RoI) [23]. These unwanted elements within liver images pose challenges for real-time segmentation implementation, detrimentally affecting segmentation accuracy [9]. Processing medical images entails managing vast amounts of data, often taxing memory resources [24]. When executed on a graphics processing unit (GPU), insufficient GPU memory hampers the segmentation process [25]. GPUs are pivotal in accelerating processes within image-guided surgery (IGS) [9; 10]. Employing cropping techniques aids in eliminating unwanted elements from medical images, enhancing overall process efficiency. We aim to achieve a rapid parallel cropped image for liver segmentation and volume assessment while minimizing intermediate memory transfers between the central processing unit (CPU) and GPU. This strategy reduces processing overhead on unnecessary image elements, obtaining significant speed improvements. Our proposal is based on the persistent thread block (PT) approach [26]. The PT approach relies on the number of computing blocks available. In cases where there are insufficient computing blocks to process image elements independently, the remaining elements' workload is distributed among existing blocks. These blocks iterate over all image elements until complete processing, utilizing a grid-stride loop described by [PERSON] et al. [26]. Persistence and grid-stride loop-based cropping offer the flexibility to harness parallelism while minimizing the processing of unwanted voxel elements. This technique consists of two parts: first, calculating diagonally opposite cropping points in a single GPU kernel call, followed by efficient data transfer from the original to the cropped image. The main contributions of this work are as follows: (1) A novel voxel-based parallel cropping algorithm is used that significantly reduces unnecessary image content before segmentation.