Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images

• URL: http://arxiv.org/abs/2207.01623v1
• Date: Mon, 4 Jul 2022 15:17:44 GMT
• Title: Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images
• Authors: Alessia De Biase, Nanna Maria Sijtsema, Lisanne van Dijk, Johannes A. Langendijk, Peter van Ooijen
• Abstract summary: Tumor segmentation is a fundamental step for radiotherapy treatment planning. This study proposes a novel automatic deep learning (DL) model to assist radiation oncologists in a slice-by-slice GTVp segmentation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oropharyngeal cancer patients (OPC), simultaneous assessment of different image modalities is needed, and each image volume is explored slice-by-slice from different orientations. Moreover, the manual fixed boundary of segmentation neglects the spatial uncertainty known to occur in tumor delineation. This study proposes a novel automatic deep learning (DL) model to assist radiation oncologists in a slice-by-slice adaptive GTVp segmentation on registered FDG PET/CT images. We included 138 OPC patients treated with (chemo)radiation in our institute. Our DL framework exploits both inter and intra-slice context. Sequences of 3 consecutive 2D slices of concatenated FDG PET/CT images and GTVp contours were used as input. A 3-fold cross validation was performed three times, training on sequences extracted from the Axial (A), Sagittal (S), and Coronal (C) plane of 113 patients. Since consecutive sequences in a volume contain overlapping slices, each slice resulted in three outcome predictions that were averaged. In the A, S, and C planes, the output shows areas with different probabilities of predicting the tumor. The performance of the models was assessed on 25 patients at different probability thresholds using the mean Dice Score Coefficient (DSC). Predictions were the closest to the ground truth at a probability threshold of 0.9 (DSC of 0.70 in the A, 0.77 in the S, and 0.80 in the C plane). The promising results of the proposed DL model show that the probability maps on registered FDG PET/CT images could guide radiation oncologists in a slice-by-slice adaptive GTVp segmentation.

Related papers

• Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images [59.85702949046042]
  We present a weakly supervised method to generate a healthy version of a diseased image and then use it to obtain a pixel-wise anomaly map. We employ a diffusion model trained on healthy samples and combine Denoising Diffusion Probabilistic Model (DDPM) and Denoising Implicit Model (DDIM) at each step of the sampling process. We verify that when our method is applied to healthy samples, the input images are reconstructed without significant modifications.
  arXiv  Detail & Related papers  (2023-08-03T21:56:50Z)
• Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling [66.75096111651062]
  We created a large-scale dataset of 10,021 thoracic CTs with 157 labels. We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. Our resulting segmentation models demonstrated remarkable performance on CXR.
  arXiv  Detail & Related papers  (2023-06-06T18:01:08Z)
• Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks [47.187609203210705]
  We aim to develop and validate an automated computational framework for patient-specific deposition modelling. An image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images.
  arXiv  Detail & Related papers  (2023-03-02T07:47:07Z)
• Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks [0.0]
  We propose an automatic algorithm aimed at volumetric segmentation of acute ischemic stroke lesion in non-contrast computed tomography brain 3D images. Our deep-learning approach is based on the popular 3D U-Net convolutional neural network architecture.
  arXiv  Detail & Related papers  (2023-01-17T10:39:39Z)
• Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks [2.735686397209314]
  The goal of this study was to report the performance of a deep neural network designed to automatically segment regions suspected of cancer in whole-body 18F-FDG PET/CT images. A cascaded approach was developed where a stacked ensemble of 3D UNET CNN processed the PET/CT images at a fixed 6mm resolution.
  arXiv  Detail & Related papers  (2022-10-14T19:25:56Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks [0.4433315630787158]
  We used a 3D nnU-Net model with residual layers supplemented by squeeze and excitation (SE) normalization for tumor segmentation from PET/CT images. A hazard risk prediction model (CoxCC) was trained on a number of PET/CT radiomic features extracted from the segmented lesions. A 10-fold cross-validated CoxCC model resulted in a c-index validation score of 0.89, and a c-index score 0.61 on the HECKTOR challenge test dataset.
  arXiv  Detail & Related papers  (2022-02-16T01:59:33Z)
• 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)
• PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images [19.725599681891925]
  We propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation. The proposed method shows its robustness and generalization capability in different datasets and different backbones.
  arXiv  Detail & Related papers  (2021-02-27T17:30:09Z)
• Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification [23.075722503902714]
  We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymph nodes. We propose a divide-and-conquer decision stratification approach that divides OSLNs into tumor-proximal and tumor-distal categories. We present a novel global-local network (GLNet) that combines high-level lesion characteristics with features learned from localized 3D image patches.
  arXiv  Detail & Related papers  (2020-05-27T23:12:11Z)
• Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT [48.785596536318884]
  The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
  arXiv  Detail & Related papers  (2020-04-02T21:49:14Z)

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.