Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting

• URL: http://arxiv.org/abs/2305.14351v1
• Date: Fri, 28 Apr 2023 12:40:01 GMT
• Title: Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting
• Authors: David Bouget, Demah Alsinan, Valeria Gaitan, Ragnhild Holden Helland, Andr\'e Pedersen, Ole Solheim and Ingerid Reinertsen
• Abstract summary: The Raidionics software is an open-source tool for standardized and automatic tumor segmentation and generation of clinical reports. The software includes preoperative and postsurgical segmentation models for all major tumor types. The generation of a standardized clinical report, including the tumor segmentation and features, requires about ten minutes on a regular laptop.
• Score: 0.1759008116536278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance (MR) scans. Currently, the lack of open tools for standardized and automatic tumor segmentation and generation of clinical reports, incorporating relevant tumor characteristics, leads to potential risks from inherent decisions' subjectivity. To tackle this problem, the proposed Raidionics open-source software has been developed, offering both a user-friendly graphical user interface and stable processing backend. The software includes preoperative segmentation models for each of the most common tumor types (i.e., glioblastomas, lower grade gliomas, meningiomas, and metastases), together with one early postoperative glioblastoma segmentation model. Preoperative segmentation performances were quite homogeneous across the four different brain tumor types, with an average Dice around 85% and patient-wise recall and precision around 95%. Postoperatively, performances were lower with an average Dice of 41%. Overall, the generation of a standardized clinical report, including the tumor segmentation and features computation, requires about ten minutes on a regular laptop. The proposed Raidionics software is the first open solution enabling an easy use of state-of-the-art segmentation models for all major tumor types, including preoperative and postsurgical standardized reports.

Related papers

• Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation [47.119513326344126]
  The BraTS-MEN-RT challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space. Target volume annotations adhere to established radiotherapy planning protocols.
  arXiv  Detail & Related papers  (2024-05-28T17:25:43Z)
• 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)
• Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI [2.9746083684997418]
  This study introduces a novel data augmentation technique, aimed at automating spine tumor segmentation and localization through AI approaches. A Convolutional Neural Network (CNN) architecture is employed for tumor classification. 3D vertebral segmentation and labeling techniques are used to help pinpoint the exact location of the tumors in the lumbar spine. Results indicate a remarkable performance, with 99% accuracy for tumor segmentation, 98% accuracy for tumor classification, and 99% accuracy for tumor localization achieved with the proposed approach.
  arXiv  Detail & Related papers  (2024-05-07T05:55:50Z)
• Cross-modality Guidance-aided Multi-modal Learning with Dual Attention for MRI Brain Tumor Grading [47.50733518140625]
  Brain tumor represents one of the most fatal cancers around the world, and is very common in children and the elderly. We propose a novel cross-modality guidance-aided multi-modal learning with dual attention for addressing the task of MRI brain tumor grading.
  arXiv  Detail & Related papers  (2024-01-17T07:54:49Z)
• Automated ensemble method for pediatric brain tumor segmentation [0.0]
  This study introduces a novel ensemble approach using ONet and modified versions of UNet. Data augmentation ensures robustness and accuracy across different scanning protocols. Results indicate that this advanced ensemble approach offers promising prospects for enhanced diagnostic accuracy.
  arXiv  Detail & Related papers  (2023-08-14T15:29:32Z)
• Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks [33.51490233427579]
  Two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.
  arXiv  Detail & Related papers  (2023-04-18T10:14:45Z)
• Preoperative brain tumor imaging: models and software for segmentation and standardized reporting [0.0]
  We investigate glioblastomas, lower grade gliomas, meningiomas, and metastases through four cohorts of up to 4000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Two software solutions have been developed, enabling an easy use of the trained models and standardized clinical reports.
  arXiv  Detail & Related papers  (2022-04-29T16:29:17Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark [48.30502612686276]
  Lung cancer is one of the deadliest cancers, and its effective diagnosis and treatment depend on the accurate delineation of the tumor. Human-centered segmentation, which is currently the most common approach, is subject to inter-observer variability. The 2018 VIP Cup started with a global engagement from 42 countries to access the competition data. In a nutshell, all the algorithms proposed during the competition, are based on deep learning models combined with a false positive reduction technique.
  arXiv  Detail & Related papers  (2022-01-03T03:06:38Z)
• 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.