Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline

Abstract

Delineation of Computed Tomography (CT) abdominal anatomical structure, specifically spleen segmentation, is useful for not only measuring tissue volume and biomarkers but also for monitoring interventions. Recently, segmentation algorithms using deep learning have been widely used to reduce time humans spend to label CT data. However, the computerized segmentation has two major difficulties: managing intermediate results (e.g., resampled scans, 2D sliced image for deep learning), and setting up the system environments and packages for autonomous execution. To overcome these issues, we propose an automated pipeline for the abdominal spleen segmentation. This pipeline provides an end-to-end synthesized process that allows users to avoid installing any packages and to deal with the intermediate results locally. The pipeline has three major stages: pre-processing of input data, segmentation of spleen using deep learning, 3D reconstruction with the generated labels by matching the segmentation results with the original image dimensions, which can then be used later and for display or demonstration.

A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease

“…A variety of deep-learning algorithms have been applied for spleen segmentation, including 2D and 3D U-Net based models, some of which combine a post-processing pipeline [ 15 , 16 , 17 , 20 , 22 , 23 , 25 , 26 , 28 , 29 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. More advanced methods include the use of transformers [ 24 , 31 ].…”

“…In some studies, this method has demonstrated a relatively high level of accuracy, with a mean error of 6% in the spleen volume calculation in the testing patient cohorts, and a strong correlation of the axial measurements with the spleen volume [ 12 , 13 ]. However, in other studies, especially in cases involving individuals with splenomegaly or spleen disorders, this method has been shown to produce relatively large errors, with a low correlation between the axial measurements and the spleen volume [ 14 , 15 , 16 ], as the shape of the spleen can significantly deviate from the formula of volume–shape approximation. Alternatively, for an accurate spleen volume estimation, manual segmentation should be performed by a radiologist for the spleen for each MRI slice, which has been demonstrated to be time-consuming and labor-intensive [ 16 , 17 , 18 ].…”

“…However, in other studies, especially in cases involving individuals with splenomegaly or spleen disorders, this method has been shown to produce relatively large errors, with a low correlation between the axial measurements and the spleen volume [ 14 , 15 , 16 ], as the shape of the spleen can significantly deviate from the formula of volume–shape approximation. Alternatively, for an accurate spleen volume estimation, manual segmentation should be performed by a radiologist for the spleen for each MRI slice, which has been demonstrated to be time-consuming and labor-intensive [ 16 , 17 , 18 ]. Consequently, there is a pressing need for a more accurate, consistent, and time-efficient approach to estimating liver and spleen volumes in patients with GD.…”

“…Deep learning in biomedical applications has been utilized successfully, in various studies, for abdominal organ segmentation [ 15 , 16 , 17 , 18 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. In the present study, our focus is to employ deep-learning techniques for the precise segmentation of the spleen, and the calculation of the spleen volume, using the MRI scans of 20 patients with GD as a testing cohort.…”

Identifying Periampullary Regions in MRI Images Using Deep Learning

“…In this work, we developed deep learning structures to automatically segment the PA region using MRI T1 and T2 images. Recently, there were abundant reported studies developing AI algorithms for segmentation of abdominal organs or structures including pancreas (16), liver (17,18), spleen (35,36), gallbladder (37), kidney (38,39), the local lesions of stomach (40), etc. However, there is no report of PA region segmentation using AI algorithms.…”