LAUSR

BACKGROUND Fast and accurate multi-organs segmentation from CT scans is essential for radiation treatment planning. Self-attention based deep learning methodologies provide higher accuracies than standard methods but require memory and computationally intensive calculations, which restricts their use to relatively shallow networks. PURPOSE Our goal was to develop and test a new computationally fast and memory efficient bi-directional self-attention method called nested block self attention (NBSA), which is applicable to shallow and deep multi-organ segmentation networks. METHODS A new multi-organ segmentation method combining a deep multiple resolution residual network with computationally efficient self attention called nested block self attention (MRRN-NBSA) was developed and evaluated to segment 18 different organs from head and neck (HN) and abdomen organs. MRRN-NBSA combines features from multiple image resolutions and feature levels with self-attention to extract organ specific contextual features. Computationally efficiency is achieved by using memory blocks of fixed spatial extent for self-attention calculation combined with bi-directional attention flow. Separate models were trained for HN (n = 238) and abdomen (n = 30) and tested on set aside open-source grand challenge datasets for HN (n = 10) using public domain database of computational anatomy and blinded testing on 20 cases from Beyond the Cranial Vault dataset with overall accuracy provided by the grand challenge website for abdominal organs. Robustness to two-rater segmentations was also evaluated for HN cases using the open-source dataset. Statistical comparison of MRRN-NBSA against Unet, convolutional network based self attention using criss-cross attention (CCA), dual self-attention, and transformer-based (UNETR) methods was done by measuring the differences in the average Dice similarity coefficient (DSC) accuracy for all HN organs using the Kruskall-Wallis test, followed by individual method comparisons using paired, two-sided Wilcoxon-signed rank tests at 95% confidence level with Bonferroni correction used for multiple comparsions. RESULTS MRRN-NBSA produced an average high DSC of 0.88 for HN and 0.86 for the abdomen that exceeded current methods. MRRN-NBSA was more accurate than the computationally most efficient CCA (average DSC of 0.845 for HN, 0.727 for abdomen). Kruskal-Wallis test showed significant difference between evaluated methods (p=0.00025). Pair-wise comparisons showed significant differences between MRRN-NBSA than Unet (p=0.0003), CCA (p=0.030), dual (p=0.038), and UNETR methods (p=0.012) after Bonferroni correction. MRRN-NBSA produced less variable segmentations for submandibular glands (0.82 ± 0.06) compared to two raters (0.75 ± 0.31). CONCLUSIONS MRRN-NBSA produced more accurate multi-organ segmentations than current methods on two different public datasets. Testing on larger institutional cohorts is required to establish feasibility for clinical use. This article is protected by copyright. All rights reserved.