Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings最新文献

{"title":"Head and Neck Tumor Segmentation for MRI-Guided Radiation Therapy Using Pre-trained STU-Net Models.","authors":"Zihao Wang, Mengye Lyu","doi":"10.1007/978-3-031-83274-1_4","DOIUrl":"https://doi.org/10.1007/978-3-031-83274-1_4","url":null,"abstract":"<p><p>Accurate segmentation of tumors in MRI-guided radiation therapy (RT) is crucial for effective treatment planning, particularly for complex malignancies such as head and neck cancer (HNC). This study presents a comparative analysis between two state-of-the-art deep learning models, nnU-Net v2 and STU-Net, for automatic tumor segmentation in pre-RT MRI images. While both models are designed for medical image segmentation, STU-Net introduces critical improvements in scalability and transferability, with parameter sizes ranging from 14 million to 1.4 billion. Leveraging large-scale pre-training on datasets such as TotalSegmentator, STU-Net captures complex and variable tumor structures more effectively. We modified the default nnU-Net v2 by adding additional convolutional layers to both the encoder and decoder, improving its performance for MRI data. Based on our experimental results, STU-Net demonstrated better performance than nnU-Net v2 in the head and neck tumor segmentation challenge. These findings suggest that integrating advanced models like STU-Net into clinical work-flows could remarkably enhance the precision of RT planning, potentially improving patient outcomes. Ultimately, the performance of the fine-tuned STU-Net-B model submitted for the final evaluation phase of Task 1 in this challenge achieved a DSCagg-GTVp of 0.76, a DSCagg-GTVn of 0.85, and an overall DSCagg-mean score of 0.81, securing ninth place in the Task 1 rankings. The described solution is by team SZTU-SingularMatrix for Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 challenge. Link to the trained model weights: https://github.com/Duskwang/Weight/releases.</p>","PeriodicalId":520475,"journal":{"name":"Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings","volume":"15273 ","pages":"65-74"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11983000/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144016208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge.","authors":"Kareem A Wahid, Cem Dede, Dina M El-Habashy, Serageldin Kamel, Michael K Rooney, Yomna Khamis, Moamen R A Abdelaal, Sara Ahmed, Kelsey L Corrigan, Enoch Chang, Stephanie O Dudzinski, Travis C Salzillo, Brigid A McDonald, Samuel L Mulder, Lucas McCullum, Qusai Alakayleh, Carlos Sjogreen, Renjie He, Abdallah S R Mohamed, Stephen Y Lai, John P Christodouleas, Andrew J Schaefer, Mohamed A Naser, Clifton D Fuller","doi":"10.1007/978-3-031-83274-1_1","DOIUrl":"10.1007/978-3-031-83274-1_1","url":null,"abstract":"<p><p>Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. This challenge addresses the scarcity of large, publicly available AI-ready adaptive RT datasets in HNC and explores the potential of incorporating multi-timepoint data to enhance RT auto-segmentation performance. Participants tackled two HNC segmentation tasks: automatic delineation of primary gross tumor volume (GTVp) and gross metastatic regional lymph nodes (GTVn) on pre-RT (Task 1) and mid-RT (Task 2) T2-weighted scans. The challenge provided 150 HNC cases for training and 50 for final testing hosted on grand-challenge.org using a Docker submission framework. In total, 19 independent teams from across the world qualified by submitting both their algorithms and corresponding papers, resulting in 18 submissions for Task 1 and 15 submissions for Task 2. Evaluation using the mean aggregated Dice Similarity Coefficient showed top-performing AI methods achieved scores of 0.825 in Task 1 and 0.733 in Task 2. These results surpassed clinician interobserver variability benchmarks, marking significant strides in automated tumor segmentation for MR-guided RT applications in HNC.</p>","PeriodicalId":520475,"journal":{"name":"Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings","volume":"15273 ","pages":"1-35"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11925392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Head and Neck Tumor Segmentation Using Pre-RT MRI Scans and Cascaded DualUNet.","authors":"Mikko Saukkoriipi, Jaakko Sahlsten, Joel Jaskari, Ahmed Al-Tahmeesschi, Laura Ruotsalainen, Kimmo Kaski","doi":"10.1007/978-3-031-83274-1_14","DOIUrl":"https://doi.org/10.1007/978-3-031-83274-1_14","url":null,"abstract":"<p><p>Accurate segmentation of the primary gross tumor volumes and metastatic lymph nodes in head and neck cancer is crucial for radiotherapy but remains challenging due to high interobserver variability, highlighting a need for an effective auto-segmentation tool. Tumor delineation is used throughout radiotherapy for treatment planning, initially for pre-radiotherapy (pre-RT) MRI scans followed-up by mid-radiotherapy (mid-RT) during the treatment. For the pre-RT task, we propose a dual-stage 3D UNet approach using cascaded neural networks for progressive accuracy refinement. The first-stage models produce an initial binary segmentation, which is then refined with an ensemble of second-stage models for a multiclass segmentation. In Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Task 1, we utilize a dataset consisting of pre-RT and mid-RT T2-weighted MRI scans. The method is trained using 5-fold cross-validation and evaluated as an ensemble of five coarse models and ten refinement models. Our approach (team FinoxyAI) achieves a mean aggregated Dice similarity coefficient of 0.737 on the test set. Moreover, with this metric, our dual-stage approach highlights consistent improvement in segmentation performance across all folds compared to a single-stage segmentation method.</p>","PeriodicalId":520475,"journal":{"name":"Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings","volume":"15273 ","pages":"191-203"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12050416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced nnU-Net Architectures for Automated MRI Segmentation of Head and Neck Tumors in Adaptive Radiation Therapy.","authors":"Jessica Kächele, Maximilian Zenk, Maximilian Rokuss, Constantin Ulrich, Tassilo Wald, Klaus H Maier-Hein","doi":"10.1007/978-3-031-83274-1_3","DOIUrl":"https://doi.org/10.1007/978-3-031-83274-1_3","url":null,"abstract":"<p><p>The increasing utilization of MRI in radiation therapy planning for head and neck cancer (HNC) highlights the need for precise tumor segmentation to enhance treatment efficacy and reduce side effects. This work presents segmentation models developed for the HNTS-MRG 2024 challenge by the team mic-dkfz, focusing on automated segmentation of HNC tumors from MRI images at two radiotherapy (RT) stages: before (pre-RT) and 2-4 weeks into RT (mid-RT). For Task 1 (pre-RT segmentation), we built upon the nnU-Net framework, enhancing it with the larger Residual Encoder architecture. We incorporated extensive data augmentation and applied transfer learning by pre-training the model on a diverse set of public 3D medical imaging datasets. For Task 2 (mid-RT segmentation), we adopted a longitudinal approach by integrating registered pre-RT images and their segmentations as additional inputs into the nnU-Net framework. On the test set, our models achieved mean aggregated Dice Similarity Coefficient (aggDSC) scores of 81.2 for Task 1 and 72.7 for Task 2. Especially the primary tumor (GTVp) segmentation is challenging and presents potential for further optimization. These results demonstrate the effectiveness of combining advanced architectures, transfer learning, and longitudinal data integration for automated tumor segmentation in MRI-guided adaptive radiation therapy.</p>","PeriodicalId":520475,"journal":{"name":"Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings","volume":"15273 ","pages":"50-64"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12023904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143998732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI-Based Head and Neck Tumor Segmentation Using nnU-Net with 15-Fold Cross-Validation Ensemble.","authors":"Frank N Mol, Luuk van der Hoek, Baoqiang Ma, Bharath Chowdhary Nagam, Nanna M Sijtsema, Lisanne V van Dijk, Kerstin Bunte, Rifka Vlijm, Peter M A van Ooijen","doi":"10.1007/978-3-031-83274-1_13","DOIUrl":"https://doi.org/10.1007/978-3-031-83274-1_13","url":null,"abstract":"<p><p>The superior soft tissue differentiation provided by MRI may enable more accurate tumor segmentation compared to CT and PET, potentially enhancing adaptive radiotherapy treatment planning. The Head and Neck Tumor Segmentation for MR-Guided Applications challenge (HNTSMRG-24) comprises two tasks: segmentation of primary gross tumor volume (GTVp) and metastatic lymph nodes (GTVn) on T2-weighted MRI volumes obtained at (1) pre-radiotherapy (pre-RT) and (2) mid-radiotherapy (mid-RT). The training dataset consists of data from 150 patients, including MRI volumes of pre-RT, mid-RT, and pre-RT registered to the corresponding mid-RT volumes. Each MRI volume is accompanied by a label mask, generated by merging independent annotations from a minimum of three experts. For both tasks, we propose adopting the nnU-Net V2 framework by the use of a 15-fold cross-validation ensemble instead of the standard number of 5 folds for increased robustness and variability. For pre-RT segmentation, we augmented the initial training data (150 pre-RT volumes and masks) with the corresponding mid-RT data. For mid-RT segmentation, we opted for a three-channel input, which, in addition to the mid-RT MRI volume, comprises the registered pre-RT MRI volume and the corresponding mask. The mean of the aggregated Dice Similarity Coefficient for GTVp and GTVn is computed on a blind test set and determines the quality of the proposed methods. These metrics determine the final ranking of methods for both tasks separately. The final blind testing (50 patients) of the methods proposed by our team, <i>RUG</i>_<i>UMCG</i>, resulted in an aggregated Dice Similarity Coefficient of 0.81 (0.77 for GTVp and 0.85 for GTVn) for Task 1 and 0.70 (0.54 for GTVp and 0.86 for GTVn) for Task 2.</p>","PeriodicalId":520475,"journal":{"name":"Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings","volume":"15273 ","pages":"179-190"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12018675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143998857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the U-Net Configuration for Automated Delineation of Head and Neck Cancer on MRI.","authors":"Andrei Iantsen","doi":"10.1007/978-3-031-83274-1_18","DOIUrl":"https://doi.org/10.1007/978-3-031-83274-1_18","url":null,"abstract":"<p><p>Tumor volume segmentation on MRI is a challenging and time-consuming process that is performed manually in typical clinical settings. This work presents an approach to automated delineation of head and neck tumors on MRI scans, developed in the context of the MICCAI Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge. Rather than designing a new, task-specific convolutional neural network, the focus of this research was to propose improvements to the configuration commonly used in medical segmentation tasks, relying solely on the traditional U-Net architecture. The empirical results presented in this article suggest the superiority of patch-wise normalization used for both training and sliding window inference. They also indicate that the performance of segmentation models can be enhanced by applying a scheduled data augmentation policy during training. Finally, it is shown that a small improvement in quality can be achieved by using Gaussian weighting to combine predictions for individual patches during sliding window inference. The model with the best configuration obtained an aggregated Dice Similarity Coefficient <math> <mrow> <mfenced> <mrow> <msub><mrow><mtext>DSC</mtext></mrow> <mrow><mtext>agg</mtext></mrow> </msub> </mrow> </mfenced> </mrow> </math> of 0.749 in Task 1 and 0.710 in Task 2 on five cross-validation folds. The ensemble of five models (one best model per validation fold) showed consistent results on a private test set of 50 patients with an <math> <mrow> <msub><mrow><mtext>DSC</mtext></mrow> <mrow><mtext>agg</mtext></mrow> </msub> </mrow> </math> of 0.752 in Task 1 and 0.718 in Task 2 (team name: andrei.iantsen). The source code and model weights are freely available at www.github.com/iantsen/hntsmrg.</p>","PeriodicalId":520475,"journal":{"name":"Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings","volume":"15273 ","pages":"230-240"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12053535/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Coarse-to-Fine Framework for Mid-Radiotherapy Head and Neck Cancer MRI Segmentation.","authors":"Jing Ni, Qiulei Yao, Yanfei Liu, Haikun Qi","doi":"10.1007/978-3-031-83274-1_11","DOIUrl":"https://doi.org/10.1007/978-3-031-83274-1_11","url":null,"abstract":"<p><p>Radiotherapy is the preferred treatment modality for head and neck cancer (HNC). During the treatment, adaptive radiation therapy (ART) technology is commonly employed to account for changes in target volume and alterations in patient anatomy. This adaptability ensures that treatment remains precise and effective despite these physiological variations. Magnetic resonance imaging (MRI) provides higher-resolution soft tissue images, making it valuable in target delineation of HNC treatment. The delineation in ART should adhere to the same principles as those used in the initial delineation. Consequently, the contouring performed on MR images during ART should reference the earlier delineations for consistency and accuracy. To address this, we proposed a coarse-to-fine cascade framework based on 3D U-Net to segment mid-radiotherapy HNC from T2-weighted MRI. The model consists of two interconnected components: a coarse segmentation network and a fine segmentation network, both sharing the same architecture. In the coarse segmentation phase, different forms of prior information were used as input, including dilated pre-radiotherapy masks. In the fine segmentation phase, a resampling operation based on a bounding box focuses on the region of interest, refining the prediction with the mid-radiotherapy image to achieve the final segmentation. In our experiment, the final results were achieved with an aggregated Dice Similarity Coefficient (DSC) of 0.562, indicating that the prior information plays a crucial role in enhancing segmentation accuracy. (Team name: TNL_skd).</p>","PeriodicalId":520475,"journal":{"name":"Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings","volume":"15273 ","pages":"154-165"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12010034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144037359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Self-supervised xLSTM-UNet Architectures for Head and Neck Tumor Segmentation in MR-Guided Applications.","authors":"Abdul Qayyum, Moona Mazher, Steven A Niederer","doi":"10.1007/978-3-031-83274-1_12","DOIUrl":"10.1007/978-3-031-83274-1_12","url":null,"abstract":"<p><p>Radiation therapy (RT) plays a pivotal role in treating head and neck cancer (HNC), with MRI-guided approaches offering superior soft tissue contrast and daily adaptive capabilities that significantly enhance treatment precision while minimizing side effects. To optimize MRI-guided adaptive RT for HNC, we propose a novel two-stage model for Head and Neck Tumor Segmentation. In the first stage, we leverage a Self-Supervised 3D Student-Teacher Learning Framework, specifically utilizing the DINOv2 architecture, to learn effective representations from a limited unlabeled dataset. This approach effectively addresses the challenge posed by the scarcity of annotated data, enabling the model to generalize better in tumor identification and segmentation. In the second stage, we fine-tune an xLSTM-based UNet model that is specifically designed to capture both spatial and sequential features of tumor progression. This hybrid architecture improves segmentation accuracy by integrating temporal dependencies, making it particularly well-suited for MRI-guided adaptive RT planning in HNC. The model's performance is rigorously evaluated on a diverse set of HNC cases, demonstrating significant improvements over state-of-the-art deep learning models in accurately segmenting tumor structures. Our proposed solution achieved an impressive mean aggregated Dice Coefficient of 0.81 for pre-RT segments and 0.65 for mid-RT segments, underscoring its effectiveness in automated segmentation tasks. This work advances the field of HNC imaging by providing a robust, generalizable solution for automated Head and Neck Tumor Segmentation, ultimately enhancing the quality of care for patients undergoing RT. Our team name is DeepLearnAI (CEMRG). The code for this work is available at https://github.com/RespectKnowledge/SSL-based-DINOv2_Vision-LSTM_Head-and-Neck-Tumor_Segmentation.</p>","PeriodicalId":520475,"journal":{"name":"Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings","volume":"15273 ","pages":"166-178"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12091698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy.","authors":"Jintao Ren, Kim Hochreuter, Mathis Ersted Rasmussen, Jesper Folsted Kallehauge, Stine Sofia Korreman","doi":"10.1007/978-3-031-83274-1_2","DOIUrl":"10.1007/978-3-031-83274-1_2","url":null,"abstract":"<p><p>Radiation therapy (RT) is a vital part of treatment for head and neck cancer, where accurate segmentation of gross tumor volume (GTV) is essential for effective treatment planning. This study investigates the use of pre-RT tumor regions and local gradient maps to enhance mid-RT tumor segmentation for head and neck cancer in MRI-guided adaptive radiotherapy. By leveraging pre-RT images and their segmentations as prior knowledge, we address the challenge of tumor localization in mid-RT segmentation. A gradient map of the tumor region from the pre-RT image is computed and applied to mid-RT images to improve tumor boundary delineation. Our approach demonstrated improved segmentation accuracy for both primary GTV (GTVp) and nodal GTV (GTVn), though performance was limited by data constraints. The final DSC <i>_agg</i> scores from the challenge's test set evaluation were 0.534 for GTVp, 0.867 for GTVn, and a mean score of 0.70. This method shows potential for enhancing segmentation and treatment planning in adaptive radiotherapy. Team: DCPT-Stine's group.</p>","PeriodicalId":520475,"journal":{"name":"Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings","volume":"15273 ","pages":"36-49"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11977786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Head and Neck Tumor Segmentation on MRIs with Fast and Resource-Efficient Staged nnU-Nets.","authors":"Elias Tappeiner, Christian Gapp, Martin Welk, Rainer Schubert","doi":"10.1007/978-3-031-83274-1_6","DOIUrl":"https://doi.org/10.1007/978-3-031-83274-1_6","url":null,"abstract":"<p><p>MRI-guided radiotherapy (RT) planning offers key advantages over conventional CT-based methods, including superior soft tissue contrast and the potential for daily adaptive RT due to the reduction of the radiation burden. In the Head and Neck (HN) region labor-intensive and time-consuming tumor segmentation still limits full utilization of MRI-guided adaptive RT. The HN Tumor Segmentation for MR-Guided Applications 2024 challenge (HNTS-MRG) aims to improve automatic tumor segmentation on MRI images by providing a dataset with reference annotations for the tasks of pre-RT and mid-RT planning. In this work, we present our approach for the HNTS-MRG challenge. Based on the insights of a thorough literature review we implemented a fast and resource-efficient two-stage segmentation method using the nnU-Net architecture with residual encoders as a backbone. In our two-stage approach we use the segmentation results of a first training round to guide the sampling process for a second refinement stage. For the pre-RT task, we achieved competitive results using only the first-stage nnU-Net. For the mid-RT task, we could significantly increase the segmentation performance of the basic first stage nnU-Net by utilizing the prior knowledge of the pre-RT plan as an additional input for the second stage refinement network. As team alpinists we achieved an aggregated Dice Coefficient of 80.97 for the pre-RT and 69.84 for the mid-RT task on the online test set of the challenge. Our code and trained model weights for the two-stage nnU-Net approach with residual encoders are available at https://github.com/elitap/hntsmrg24.</p>","PeriodicalId":520475,"journal":{"name":"Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings","volume":"15273 ","pages":"87-98"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11979668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144037318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}