Convolutional neural network based deep-learning architecture for intraprostatic tumour contouring on PSMA PET images in patients with primary prostate cancer

• URL: http://arxiv.org/abs/2008.03201v1
• Date: Fri, 7 Aug 2020 14:32:14 GMT
• Title: Convolutional neural network based deep-learning architecture for intraprostatic tumour contouring on PSMA PET images in patients with primary prostate cancer
• Authors: Dejan Kostyszyn, Tobias Fechter, Nico Bartl, Anca L. Grosu, Christian Gratzke, August Sigle, Michael Mix, Juri Ruf, Thomas F. Fassbender, Selina Kiefer, Alisa S. Bettermann, Nils H. Nicolay, Simon Spohn, Maria U. Kramer, Peter Bronsert, Hongqian Guo, Xuefeng Qiu, Feng Wang, Christoph Henkenberens, Rudolf A. Werner, Dimos Baltas, Philipp T. Meyer, Thorsten Derlin, Mengxia Chen, Constantinos Zamboglou
• Abstract summary: The aim of this study was to develop a convolutional neural network (CNN) for automated segmentation of intraprostatic tumour (GTV) in PSMA-PET. The CNN was trained on [68Ga]PSMA-PET and [18F]PSMA-PET images of 152 patients from two different institutions.
• Score: 3.214308133129678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate delineation of the intraprostatic gross tumour volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific membrane antigen positron emission tomography (PSMA-PET) may outperform MRI in GTV detection. However, visual GTV delineation underlies interobserver heterogeneity and is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for automated segmentation of intraprostatic tumour (GTV-CNN) in PSMA-PET. Methods: The CNN (3D U-Net) was trained on [68Ga]PSMA-PET images of 152 patients from two different institutions and the training labels were generated manually using a validated technique. The CNN was tested on two independent internal (cohort 1: [68Ga]PSMA-PET, n=18 and cohort 2: [18F]PSMA-PET, n=19) and one external (cohort 3: [68Ga]PSMA-PET, n=20) test-datasets. Accordance between manual contours and GTV-CNN was assessed with Dice-S{\o}rensen coefficient (DSC). Sensitivity and specificity were calculated for the two internal test-datasets by using whole-mount histology. Results: Median DSCs for cohorts 1-3 were 0.84 (range: 0.32-0.95), 0.81 (range: 0.28-0.93) and 0.83 (range: 0.32-0.93), respectively. Sensitivities and specificities for GTV-CNN were comparable with manual expert contours: 0.98 and 0.76 (cohort 1) and 1 and 0.57 (cohort 2), respectively. Computation time was around 6 seconds for a standard dataset. Conclusion: The application of a CNN for automated contouring of intraprostatic GTV in [68Ga]PSMA- and [18F]PSMA-PET images resulted in a high concordance with expert contours and in high sensitivities and specificities in comparison with histology reference. This robust, accurate and fast technique may be implemented for treatment concepts in primary PCa. The trained model and the study's source code are available in an open source repository.

Related papers

• Towards AI Lesion Tracking in PET/CT Imaging: A Siamese-based CNN Pipeline applied on PSMA PET/CT Scans [2.3432822395081807]
  This work introduces a Siamese CNN approach for lesion tracking between PET/CT scans. Our algorithm extracts suitable lesion patches and forwards them into a Siamese CNN trained to classify the lesion patch pairs as corresponding or non-corresponding lesions. Experiments have been performed with different input patch types and a Siamese network in 2D and 3D.
  arXiv  Detail & Related papers  (2024-06-13T17:06:15Z)
• Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge [44.586530244472655]
  We describe the design and results from the BraTS 2023 Intracranial Meningioma Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor.
  arXiv  Detail & Related papers  (2024-05-16T03:23:57Z)
• Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Patients Using a Longitudinally-Aware Segmentation Network [7.225391135995692]
  longitudinally-aware segmentation network (LAS-Net) can quantify serial PET/CT images for pediatric Hodgkin lymphoma patients. LAS-Net incorporates longitudinal cross-attention, allowing relevant features from PET1 to inform the analysis of PET2. LAS-Net detected residual lymphoma in PET2 with an F1 score of 0.606.
  arXiv  Detail & Related papers  (2024-04-12T17:20:57Z)
• Neural Network-Based Histologic Remission Prediction In Ulcerative Colitis [38.150634108667774]
  Histologic remission is a new therapeutic target in ulcerative colitis (UC) Endocytoscopy (EC) is a novel ultra-high magnification endoscopic technique. We propose a neural network model that can assess histological disease activity in EC images.
  arXiv  Detail & Related papers  (2023-08-28T15:54:14Z)
• An open-source deep learning algorithm for efficient and fully-automatic analysis of the choroid in optical coherence tomography [3.951995351344523]
  We develop an open-source, fully-automatic deep learning algorithm, DeepGPET, for choroid region segmentation in optical coherence tomography ( OCT) data.
  arXiv  Detail & Related papers  (2023-07-03T10:01:36Z)
• Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification [52.77024349608834]
  To investigate chest radiograph (CXR) classification performance of vision transformers (ViT) and interpretability of attention-based saliency. ViTs were fine-tuned for lung disease classification using four public data sets: CheXpert, Chest X-Ray 14, MIMIC CXR, and VinBigData. ViTs had comparable CXR classification AUCs compared with state-of-the-art CNNs.
  arXiv  Detail & Related papers  (2023-03-03T12:05:41Z)
• Multimodal Deep Learning to Differentiate Tumor Recurrence from Treatment Effect in Human Glioblastoma [2.726462580631231]
  Differentiating tumor progression (TP) from treatment-related necrosis (TN) is critical for clinical management decisions in glioblastoma (GBM) dPET includes novel methods of a model-corrected blood input function that accounts for partial volume averaging to compute parametric maps that reveal kinetic information. CNN was trained to predict classification accuracy between TP and TN for $35$ brain tumors from $26$ subjects in the PET-MR image space.
  arXiv  Detail & Related papers  (2023-02-27T20:12:28Z)
• 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)
• 3D Structural Analysis of the Optic Nerve Head to Robustly Discriminate Between Papilledema and Optic Disc Drusen [44.754910718620295]
  We developed a deep learning algorithm to identify major tissue structures of the optic nerve head (ONH) in 3D optical coherence tomography ( OCT) scans. A classification algorithm was designed using 150 OCT volumes to perform 3-class classifications (1: ODD, 2: papilledema, 3: healthy) strictly from their drusen and prelamina swelling scores. Our AI approach accurately discriminated ODD from papilledema, using a single OCT scan.
  arXiv  Detail & Related papers  (2021-12-18T17:05:53Z)
• COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19 [39.57518533765393]
  There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. We present an end-to-end multitask learning framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19.
  arXiv  Detail & Related papers  (2020-12-10T08:30:46Z)
• Machine Learning Automatically Detects COVID-19 using Chest CTs in a Large Multicenter Cohort [43.99203831722203]
  Our retrospective study obtained 2096 chest CTs from 16 institutions. A metric-based approach for classification of COVID-19 used interpretable features. A deep learning-based classifier differentiated COVID-19 via 3D features extracted from CT attenuation and probability distribution of airspace opacities.
  arXiv  Detail & Related papers  (2020-06-09T00:40:35Z)

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.