A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy

• URL: http://arxiv.org/abs/2004.13294v1
• Date: Tue, 28 Apr 2020 04:29:46 GMT
• Title: A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy
• Authors: Anjali Balagopal, Dan Nguyen, Howard Morgan, Yaochung Weng, Michael Dohopolski, Mu-Han Lin, Azar Sadeghnejad Barkousaraie, Yesenia Gonzalez, Aurelie Garant, Neil Desai, Raquibul Hannan, Steve Jiang
• Abstract summary: In post-operative radiotherapy for prostate cancer, the cancerous prostate gland has been surgically removed, so the clinical target volume (CTV) to be irradiated encompasses the microscopic spread of tumor cells. In current clinical practice, physicians segment CTVs manually based on their relationship with nearby organs and other clinical information. Here, we propose a deep learning model to overcome this problem by segmenting nearby organs first, then using their relationship with the CTV to assist CTV segmentation.
• Score: 0.6262579136517118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In post-operative radiotherapy for prostate cancer, the cancerous prostate gland has been surgically removed, so the clinical target volume (CTV) to be irradiated encompasses the microscopic spread of tumor cells, which cannot be visualized in typical clinical images such as computed tomography or magnetic resonance imaging. In current clinical practice, physicians segment CTVs manually based on their relationship with nearby organs and other clinical information, per clinical guidelines. Automating post-operative prostate CTV segmentation with traditional image segmentation methods has been a major challenge. Here, we propose a deep learning model to overcome this problem by segmenting nearby organs first, then using their relationship with the CTV to assist CTV segmentation. The model proposed is trained using labels clinically approved and used for patient treatment, which are subject to relatively large inter-physician variations due to the absence of a visual ground truth. The model achieves an average Dice similarity coefficient (DSC) of 0.87 on a holdout dataset of 50 patients, much better than established methods, such as atlas-based methods (DSC<0.7). The uncertainties associated with automatically segmented CTV contours are also estimated to help physicians inspect and revise the contours, especially in areas with large inter-physician variations. We also use a 4-point grading system to show that the clinical quality of the automatically segmented CTV contours is equal to that of approved clinical contours manually drawn by physicians.

Related papers

• An Attention Based Pipeline for Identifying Pre-Cancer Lesions in Head and Neck Clinical Images [1.0957311485487375]
  Head and neck cancer is diagnosed in specialist centres after a surgical biopsy, but there is a potential for these to be missed leading to delayed diagnosis. We present an attention based pipeline that identifies suspected lesions, segments, and classifies them as non-dysplastic, dysplastic and cancerous lesions.
  arXiv  Detail & Related papers  (2024-05-03T09:02:17Z)
• Graph-based multimodal multi-lesion DLBCL treatment response prediction from PET images [0.0]
  After diagnosis, the number of nonresponding patients to standard front-line therapy remains significant (30-40%) This work aims to develop a computer-aided approach to identify high-risk patients requiring adapted treatment. We propose a method based on recent graph neural networks that combine imaging information from multiple lesions.
  arXiv  Detail & Related papers  (2023-10-25T08:16:45Z)
• Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients [60.78505216352878]
  We propose a hybrid deep neural network pipeline to predict tumour response to initial chemotherapy. We leverage a combination of representation transfer from segmentation to classification, as well as localisation and representation learning. Our approach yields a remarkably data-efficient method able to predict treatment response with a ROC-AUC of 63.7% using only 477 datasets in total.
  arXiv  Detail & Related papers  (2022-11-08T11:50:31Z)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks [0.0]
  We propose a two-step deep-learning framework to partially automate the detection of stenosis from X-ray coronary angiography images. We achieved a 0.97 accuracy on the task of classifying the Left/Right Coronary Artery angle view and 0.68/0.73 recall on the determination of the regions of interest, for LCA and RCA, respectively.
  arXiv  Detail & Related papers  (2021-03-04T11:45:54Z)
• PSA-Net: Deep Learning based Physician Style-Aware Segmentation Network for Post-Operative Prostate Cancer Clinical Target Volume [5.90921279461999]
  This study tries to determine if physician styles are consistent and learnable, if there is an impact of physician styles on treatment outcome and toxicity. A concept called physician style-aware (PSA) segmentation is proposed which is an encoder-multidecoder network trained with perceptual loss.
  arXiv  Detail & Related papers  (2021-02-15T22:42:52Z)
• Automatic Segmentation of Gross Target Volume of Nasopharynx Cancer using Ensemble of Multiscale Deep Neural Networks with Spatial Attention [2.204996105506197]
  We propose a 2.5D Convolutional Neural Network (CNN) to handle the difference of inplane and through-plane resolution. We also propose a spatial attention module to enable the network to focus on small target, and use channel attention to further improve the segmentation performance.
  arXiv  Detail & Related papers  (2021-01-27T08:20:49Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation [48.504790189796836]
  We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe) We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
  arXiv  Detail & Related papers  (2020-05-27T06:58:39Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)

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.