Paper Reading AI Learner

Deep Learning-Based Auto-Segmentation of Planning Target Volume for Total Marrow and Lymph Node Irradiation

2024-02-09 15:56:39
Ricardo Coimbra Brioso, Damiano Dei, Nicola Lambri, Daniele Loiacono, Pietro Mancosu, Marta Scorsetti


In order to optimize the radiotherapy delivery for cancer treatment, especially when dealing with complex treatments such as Total Marrow and Lymph Node Irradiation (TMLI), the accurate contouring of the Planning Target Volume (PTV) is crucial. Unfortunately, relying on manual contouring for such treatments is time-consuming and prone to errors. In this paper, we investigate the application of Deep Learning (DL) to automate the segmentation of the PTV in TMLI treatment, building upon previous work that introduced a solution to this problem based on a 2D U-Net model. We extend the previous research (i) by employing the nnU-Net framework to develop both 2D and 3D U-Net models and (ii) by evaluating the trained models on the PTV with the exclusion of bones, which consist mainly of lymp-nodes and represent the most challenging region of the target volume to segment. Our result show that the introduction of nnU-NET framework led to statistically significant improvement in the segmentation performance. In addition, the analysis on the PTV after the exclusion of bones showed that the models are quite robust also on the most challenging areas of the target volume. Overall, our study is a significant step forward in the application of DL in a complex radiotherapy treatment such as TMLI, offering a viable and scalable solution to increase the number of patients who can benefit from this treatment.

Abstract (translated)

为了优化癌症治疗中的放疗剂量,特别是当处理复杂的治疗方案时,如全身骨髓照射和淋巴结照射(TMLI),精确刻画计划靶体积(PTV)至关重要。然而,仅依赖手动轮廓进行此类治疗会花费很长时间并容易出错。在本文中,我们研究了将深度学习(DL)应用于TMLI治疗中的PTV分割,并在此基础上对之前的工作进行扩展,该工作基于2D U-Net模型解决了这个问题。我们通过使用nnU-Net框架开发2D和3D U-Net模型,以及通过评估训练后的模型对不含骨的PTV进行评估,来扩展之前的研究(i)。我们还研究了在TMLI治疗中使用nnU-NET框架对PTV进行分割后,模型的性能是否显著提高。此外,在排除骨之后对PTV的分析表明,模型在目标体积的最困难区域也表现出相当不错的鲁棒性。总的来说,我们的研究是应用深度学习在复杂放射治疗治疗中的应用迈出的重要一步,为诸如TMLI这样的复杂放射治疗提供了一个可行的、可扩展的解决方案,从而使更多的患者受益于这种治疗。



3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot