Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach

Abstract

In radiotherapy, computed tomography (CT) datasets are mostly used for radiation treatment planning to achieve a high-conformal tumor coverage while optimally sparing healthy tissue surrounding the tumor, referred to as organs-at-risk (OARs). Based on CT scan and/or magnetic resonance images, OARs have to be manually delineated by clinicians, which is one of the most time-consuming tasks in the clinical workflow. Recent multi-atlas (MA) or deep-learning (DL) based methods aim to improve the clinical routine by an automatic segmentation of OARs on a CT dataset. However, so far no studies investigated the performance of these MA or DL methods on dual-energy CT (DECT) datasets, which have been shown to improve the image quality compared to conventional 120 kVp single-energy CT. In this study, the performance of an in-house developed MA and a DL method (two-step three-dimensional U-net) was quantitatively and qualitatively evaluated on various DECT-derived pseudo-monoenergetic CT datasets ranging from 40 keV to 170 keV. At lower energies, the MA method resulted in more accurate OAR segmentations. Both the qualitative and quantitative metric analysis showed that the DL approach often performed better than the MA method.

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

“…Atlas 27,29,34,44,52,58,59,61,68,69,71,73,[78][79][80][81]84,85,87,[89][90][91]93,94,99 with shape/appearance models 38,66,76,77,82,86,92,95 with intensity models [97][98][99] with feature classification 35,63,72,75,83,86 with contour refinement 72,76,92 with level set refinement 91 Feature classification 64,74 Localization model and feature classification 51,56 Level-set statistical model 88,…”

“…68 On the other hand, specific OARs (e.g., the carotid artery) can be successfully auto-segmented only from ultrasound (US) images, 25 while the feasibility of using dual-energy CT (DECT) has been recently explored from the perspective of selecting the optimal energy level for generating the virtual monoenergetic image, 108 in which different H&N OARs can be segmented. 29…”

“…Parotid glands [22][23][24][26][27][28][29][30][31][32][34][35][36][37]40,42,[45][46][47][48][49][50][51][52][53][54][55][56][57][58]60,[63][64][65][66][68][69][70]72,73,[75][76][77][78]80,[82][83][84]86,87,90,91,93,95…”

“…Maximum distance to agreement 27,77 max b2@B dðb; @AÞ HD 95 95-percentile Hausdorff distance 22,23,[29][30][31]35,[37][38][39][40]46,49,55,58,66,69…”

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

“…Deep Learning (DL) and Multi-Atlas (MA) methods performed on Dual-Energy Computed Tomography (DECT) data have distinguished the healthy tissues from tumor tissues that are referred to as Organs-At-Risk (oARs). The Dual-Energy CT (DECT) dataset has high-resolution images as compared to single-energy CT. DL methods achieved better results for segmentation on DECT in comparison to single-energy CT for qualitative and quantitative analysis [148]. A 3D convolutional neural network deals with the partial volume averaging, inter-slice intensity variation and noise sensitivity.…”