Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT

• URL: http://arxiv.org/abs/2411.18290v2
• Date: Wed, 18 Dec 2024 07:40:45 GMT
• Title: Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT
• Authors: Zi Li, Ying Chen, Zeli Chen, Yanzhou Su, Tai Ma, Tony C. W. Mok, Yan-Jie Zhou, Yunhai Bai, Zhinlin Zheng, Le Lu, Yirui Wang, Jia Ge, Xianghua Ye, Senxiang Yan, Dakai Jin,
• Abstract summary: In the radiation therapy of nasopharyngeal carcinoma (NPC), clinicians typically delineate the gross tumor volume (GTV) using non-contrast planning computed tomography. The low contrast between tumors and adjacent normal tissues necessitates that radiation oncologists manually delineate the tumors. We propose a novel approach to directly segment NPC gross tumors on non-contrast planning CT images.
• Score: 12.199850355388214
• License:
• Abstract: In the radiation therapy of nasopharyngeal carcinoma (NPC), clinicians typically delineate the gross tumor volume (GTV) using non-contrast planning computed tomography to ensure accurate radiation dose delivery. However, the low contrast between tumors and adjacent normal tissues necessitates that radiation oncologists manually delineate the tumors, often relying on diagnostic MRI for guidance. % In this study, we propose a novel approach to directly segment NPC gross tumors on non-contrast planning CT images, circumventing potential registration errors when aligning MRI or MRI-derived tumor masks to planning CT. To address the low contrast issues between tumors and adjacent normal structures in planning CT, we introduce a 3D Semantic Asymmetry Tumor segmentation (SATs) method. Specifically, we posit that a healthy nasopharyngeal region is characteristically bilaterally symmetric, whereas the emergence of nasopharyngeal carcinoma disrupts this symmetry. Then, we propose a Siamese contrastive learning segmentation framework that minimizes the voxel-wise distance between original and flipped areas without tumor and encourages a larger distance between original and flipped areas with tumor. Thus, our approach enhances the sensitivity of features to semantic asymmetries. % Extensive experiments demonstrate that the proposed SATs achieves the leading NPC GTV segmentation performance in both internal and external testing, \emph{e.g.}, with at least 2\% absolute Dice score improvement and 12\% average distance error reduction when compared to other state-of-the-art methods in the external testing.

Related papers

• Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations [0.0]
  Surgery for brain cancer is a major problem in neurosurgery. The identification of the tumor boundaries during surgery is challenging. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images.
  arXiv  Detail & Related papers  (2024-02-11T12:58:42Z)
• A novel method to compute the contact surface area between an organ and cancer tissue [81.84413479369512]
  "contact surface area" (CSA) refers to the area of contact between a tumor and an organ. We introduce an innovative method that relies on 3D reconstructions of tumors and organs to provide an accurate and objective estimate of the CSA.
  arXiv  Detail & Related papers  (2024-01-19T14:34:34Z)
• Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer [37.55853672333369]
  This paper proposes a novel learnable neural distance that describes the precise relationship between the tumor and vessels in CT images of different patients. The developed risk marker was the strongest predictor of overall survival among preoperative factors.
  arXiv  Detail & Related papers  (2023-08-01T12:46:02Z)
• Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging [62.346649719614]
  preoperative discrimination between T2 and T3 stages is arguably both the most challenging and clinically significant task for rectal cancer treatment. We present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes.
  arXiv  Detail & Related papers  (2022-09-13T07:10:14Z)
• Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework [65.19784967388934]
  Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy. We propose a knowledge-driven framework for RLN localization, mimicking the standard approach surgeons take to identify the RLN according to its surrounding organs. Experimental results indicate that the proposed method achieves superior hit rates and substantially smaller distance errors compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-30T13:04:42Z)
• Synthetic CT Skull Generation for Transcranial MR Imaging-Guided Focused Ultrasound Interventions with Conditional Adversarial Networks [5.921808547303054]
  Transcranial MRI-guided focused ultrasound (TcMRgFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively under MRI guidance. To accurately target ultrasound through the skull, the transmitted waves must constructively interfere at the target region.
  arXiv  Detail & Related papers  (2022-02-21T11:34:29Z)
• Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images [50.55978219682419]
  We propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization.
  arXiv  Detail & Related papers  (2021-10-11T07:13:26Z)
• Contrast-enhanced MRI Synthesis Using 3D High-Resolution ConvNets [7.892005877717236]
  Gadolinium-based contrast agents (GBCAs) have been widely used to better visualize disease in brain magnetic resonance imaging (MRI) For brain tumor patients, standard-of-care includes repeated MRI with gadolinium-based contrast for disease monitoring, increasing the risk of gadolinium deposition. We present a deep learning based approach for contrast-enhanced T1 synthesis on brain tumor patients.
  arXiv  Detail & Related papers  (2021-04-04T11:54:15Z)
• Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining [73.23533486979166]
  Capsular invasion on tumor boundary has proven to be clinically correlated with the prognostic indicator, microvascular invasion (MVI) In this paper, we propose the first and novel computational framework that disentangles the task into two components.
  arXiv  Detail & Related papers  (2021-03-09T01:43:05Z)
• DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging [26.162788846435365]
  Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis. We propose a novel deep neural network for the survival prediction of resectable PDAC patients, named as 3D Contrast-Enhanced Convolutional Long Short-Term Memory network(CE-ConvLSTM) We present a multi-task CNN to accomplish both tasks of outcome and margin prediction where the network benefits from learning the tumor resection margin related features to improve survival prediction.
  arXiv  Detail & Related papers  (2020-08-26T22:51:24Z)
• Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
  We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
  arXiv  Detail & Related papers  (2020-02-03T22:25:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.