LN-Gen: Rectal Lymph Nodes Generation via Anatomical Features
- URL: http://arxiv.org/abs/2408.14977v1
- Date: Tue, 27 Aug 2024 11:40:23 GMT
- Title: LN-Gen: Rectal Lymph Nodes Generation via Anatomical Features
- Authors: Weidong Guo, Hantao Zhang, Shouhong Wan, Bingbing Zou, Wanqin Wang, Peiquan Jin,
- Abstract summary: The complexity of the surrounding anatomical structures and the scarcity of annotated data pose significant challenges.
This study introduces a novel lymph node synthesis technique aimed at generating diverse and realistic synthetic rectal lymph node samples.
- Score: 8.428364324501048
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurate segmentation of rectal lymph nodes is crucial for the staging and treatment planning of rectal cancer. However, the complexity of the surrounding anatomical structures and the scarcity of annotated data pose significant challenges. This study introduces a novel lymph node synthesis technique aimed at generating diverse and realistic synthetic rectal lymph node samples to mitigate the reliance on manual annotation. Unlike direct diffusion methods, which often produce masks that are discontinuous and of suboptimal quality, our approach leverages an implicit SDF-based method for mask generation, ensuring the production of continuous, stable, and morphologically diverse masks. Experimental results demonstrate that our synthetic data significantly improves segmentation performance. Our work highlights the potential of diffusion model for accurately synthesizing structurally complex lesions, such as lymph nodes in rectal cancer, alleviating the challenge of limited annotated data in this field and aiding in advancements in rectal cancer diagnosis and treatment.
Related papers
- Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation [6.135895757905315]
Fibrotic Lung Disease (FLD) is a severe condition marked by lung stiffening and scarring, leading to respiratory decline.
High-resolution computed tomography (HRCT) is critical for diagnosing and monitoring FLD; however, fibrosis appears as irregular, diffuse patterns with unclear boundaries.
We propose DiffSeg, a novel weakly supervised semantic segmentation (WSSS) method that uses image-level annotations to generate pixel-level fibrosis segmentation.
arXiv Detail & Related papers (2024-11-05T23:11:26Z) - Mask the Unknown: Assessing Different Strategies to Handle Weak Annotations in the MICCAI2023 Mediastinal Lymph Node Quantification Challenge [2.1994532511228773]
The MICCAI 2023 Lymph Node Quantification Challenge published the first public dataset for pathological lymph node segmentation in the mediastinum.
As lymph node annotations are expensive, the challenge was formed as a weakly supervised learning task, where only a subset of all lymph nodes in the training set have been annotated.
For the challenge submission, multiple methods for training on these weakly supervised data were explored, including noisy label training, loss masking of unlabeled data, and an approach that integrated the TotalSegmentator toolbox as a form of pseudo labeling.
Our submitted model achieved a Dice score of 0.628 and an average symmetric surface distance of
arXiv Detail & Related papers (2024-06-20T14:38:33Z) - LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas [0.010416625072338245]
The evaluation of lymph node metastases plays a crucial role in achieving precise cancer staging.
Lymph node detection poses challenges due to the presence of unclear boundaries and the diverse range of sizes and morphological characteristics.
As part of the LNQ 2023 MICCAI challenge, we propose the use of anatomical priors as a tool to address the challenges.
arXiv Detail & Related papers (2024-06-06T11:57:25Z) - Shape-aware synthesis of pathological lung CT scans using CycleGAN for enhanced semi-supervised lung segmentation [0.0]
This paper emphasizes the use of CycleGAN for unpaired image-to-image translation.
It provides an augmentation method able to generate fake pathological images matching an existing ground truth.
Preliminary results from this research demonstrate significant qualitative and quantitative improvements.
arXiv Detail & Related papers (2024-05-14T12:45:49Z) - CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation [12.226538753367965]
We present a pipeline that integrates the conditional diffusion model for lymph node generation and the nnU-Net model for lymph node segmentation.
LN-DDPM utilizes lymph node masks and anatomical structure masks as model conditions.
Experimental results on the abdominal lymph node datasets demonstrate that LN-DDPM outperforms other generative methods in the abdominal lymph node image synthesis and better assists the downstream abdominal lymph node segmentation task.
arXiv Detail & Related papers (2024-03-26T14:59:11Z) - Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted
Imaging Data via Anatomic-Conditional Controlled Latent Diffusion [68.45407109385306]
In Canada, prostate cancer is the most common form of cancer in men and accounted for 20% of new cancer cases for this demographic in 2022.
There has been significant interest in the development of deep neural networks for prostate cancer diagnosis, prognosis, and treatment planning using diffusion weighted imaging (DWI) data.
In this study, we explore the efficacy of latent diffusion for generating realistic prostate DWI data through the introduction of an anatomic-conditional controlled latent diffusion strategy.
arXiv Detail & Related papers (2023-11-30T15:11:03Z) - ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic
Diffusion Models [69.9178140563928]
Colonoscopy analysis is essential for assisting clinical diagnosis and treatment.
The scarcity of annotated data limits the effectiveness and generalization of existing methods.
We propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks.
arXiv Detail & Related papers (2023-09-03T07:55:46Z) - Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection
Segmentation System [69.40329819373954]
The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world.
At the current stage, automatically segmenting the lung infection area from CT images is essential for the diagnosis and treatment of COVID-19.
We propose a boundary guided semantic learning network (BSNet) in this paper.
arXiv Detail & Related papers (2022-09-07T05:01:38Z) - Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule
Augmentation and Detection [52.93342510469636]
Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers.
Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR.
To alleviate the limited availability of such datasets, lung nodule synthesis methods are proposed for the sake of data augmentation.
arXiv Detail & Related papers (2022-07-19T16:38:48Z) - Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents.
We generate an automatic tumor boundary detector for the rare disease of glioblastoma.
We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
arXiv Detail & Related papers (2022-04-22T17:27:00Z) - Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic
and Molecular MR Images in Patients with Post-treatment Malignant Gliomas [65.64363834322333]
Confidence Guided SAMR (CG-SAMR) synthesizes data from lesion information to multi-modal anatomic sequences.
module guides the synthesis based on confidence measure about the intermediate results.
experiments on real clinical data demonstrate that the proposed model can perform better than the state-of-theart synthesis methods.
arXiv Detail & Related papers (2020-08-06T20:20:22Z)
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.