Atlas-based methods heavily rely on the accuracy and reliability of deformable image registration, which can be very challenging due to OARs’ large shape variations, normal tissue removal, tumor growth, and image acquisition differences. Conventional atlas-based methods have been extensively explored previously 11, 12, 13, 14, 15, but significant amounts of editing efforts were found to be unavoidable 8, 16. OARs are spatially densely distributed in the H&N region and often have complex anatomical shapes, large size variations, and low CT contrasts. Therefore, automatic and accurate segmentation of a comprehensive set of H&N OARs is of great clinical benefit in this context. Moreover, because clinicians often follow the institution-specific OAR contouring style, manual delineation is easily prone to large inter-observer variations leading to differences/discrepancies in dose parameters potentially impacting the treatment outcome 7. Dosimetric information cannot be recorded for non-contoured OARs, although it is clinically important to track for analysis of post-treatment side effects 10. To shorten time expenses, many institutions choose a simplified (sometimes overly simplified) OAR protocol by contouring a small subset of OARs (e.g., only the OARs closest to the tumor). Due to the factors of patient overload and shortage of experienced physicians, long patient waiting times and/or undesirably inaccurate RT delineations are more common than necessary, reducing the treatment efficacy and safety 9. Nevertheless, precise manual delineation of this quantity of OARs is an overwhelmingly demanding task that requires great clinical expertise and time efforts, e.g., >3 h for 24 OARs 8. Recent consensus guidelines recommend a set of more than 40 OARs in the H&N region 7. This requirement demands accurate OAR delineation on the planning computed tomography (pCT) images used to configure the radiation dosage treatment. In RT, the radiation dose to normal anatomical structures, i.e., organs at risk (OARs), needs to be limited to reduce post-treatment complications, such as dry mouth, swallowing difficulties, visual damage, and cognitive decline 3, 4, 5, 6. Radiation therapy (RT) is an important and effective treatment for H&N cancer 2. Head and neck (H&N) cancer is one of the most common cancers worldwide 1. Moreover, segmentation and dosimetric accuracy are within or smaller than the inter-user variation. Crucially, multi-user studies demonstrate that 98% of SOARS predictions need only minor or no revisions to achieve clinical acceptance (reducing workloads by 90%). It consistently outperforms other state-of-the-art methods by at least 3–5% in Dice score for each institutional evaluation (up to 36% relative distance error reduction). We train SOARS using 176 patients from an internal institution and independently evaluate it on 1327 external patients across six different institutions. Here, we present an automated and highly effective stratified OAR segmentation (SOARS) system using deep learning that precisely delineates a comprehensive set of 42 H&N OARs. However, prohibitive labor costs cause most institutions to delineate a substantially smaller subset of OARs, neglecting the dose distributions of other OARs. Consensus guidelines recommend delineating over 40 OARs in the head-and-neck (H&N). 6.4.Accurate organ-at-risk (OAR) segmentation is critical to reduce radiotherapy complications.2.2 – Environments and the Global Environment.
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