Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning

• URL: http://arxiv.org/abs/2012.12820v4
• Date: Tue, 30 Mar 2021 13:32:45 GMT
• Title: Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning
• Authors: Andreanne Lemay, Charley Gros, Zhizheng Zhuo, Jie Zhang, Yunyun Duan, Julien Cohen-Adad, Yaou Liu
• Abstract summary: We present a cascaded architecture with U-Net-based models that segments tumors in a two-stage process: locate and label. The segmentation of the tumor, cavity, and edema (as a single class) reached 76.7 $pm$ 1.5% of Dice score and the segmentation of tumors alone reached 61.8 $pm$ 4.0% Dice score.
• Score: 3.2803205051531235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric quantification (size, location, growth rate) of the tumor, edema, and cavity can result in improved monitoring and treatment planning. Such quantification requires the segmentation of these structures into three separate classes. However, manual segmentation of 3-dimensional structures is time-consuming and tedious, motivating the development of automated methods. Here, we tailor a model adapted to the spinal cord tumor segmentation task. Data were obtained from 343 patients using gadolinium-enhanced T1-weighted and T2-weighted MRI scans with cervical, thoracic, and/or lumbar coverage. The dataset includes the three most common intramedullary spinal cord tumor types: astrocytomas, ependymomas, and hemangioblastomas. The proposed approach is a cascaded architecture with U-Net-based models that segments tumors in a two-stage process: locate and label. The model first finds the spinal cord and generates bounding box coordinates. The images are cropped according to this output, leading to a reduced field of view, which mitigates class imbalance. The tumor is then segmented. The segmentation of the tumor, cavity, and edema (as a single class) reached 76.7 $\pm$ 1.5% of Dice score and the segmentation of tumors alone reached 61.8 $\pm$ 4.0% Dice score. The true positive detection rate was above 87% for tumor, edema, and cavity. To the best of our knowledge, this is the first fully automatic deep learning model for spinal cord tumor segmentation. The multiclass segmentation pipeline is available in the Spinal Cord Toolbox (https://spinalcordtoolbox.com/). It can be run with custom data on a regular computer within seconds.

Related papers

• 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)
• Glioblastoma Tumor Segmentation using an Ensemble of Vision Transformers [0.0]
  Glioblastoma is one of the most aggressive and deadliest types of brain cancer. Brain Radiology Aided by Intelligent Neural NETworks (BRAINNET) generates robust tumor segmentation maks.
  arXiv  Detail & Related papers  (2023-11-09T18:55:27Z)
• 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation [52.699139151447945]
  We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation. Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
  arXiv  Detail & Related papers  (2023-06-23T12:09:52Z)
• Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning [55.789874096142285]
  We present a deep learning-based brain tumor recurrence location prediction network. We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021. Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features. Two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location.
  arXiv  Detail & Related papers  (2023-04-11T02:45:38Z)
• Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging [62.346649719614]
  preoperative discrimination between T2 and T3 stages is arguably both the most challenging and clinically significant task for rectal cancer treatment. We present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes.
  arXiv  Detail & Related papers  (2022-09-13T07:10:14Z)
• A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation [2.9364290037516496]
  An augmentation method, calledMixup, was proposed and applied to the three dimensional U-Net architecture for brain tumor segmentation. The experimental results show that the mean accuracy of Dice scores are 91.32%, 85.67%, and 82.20% respectively on the whole tumor, tumor core, and enhancing tumor segmentation.
  arXiv  Detail & Related papers  (2022-02-13T15:28:49Z)
• 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)
• Triplet Contrastive Learning for Brain Tumor Classification [99.07846518148494]
  We present a novel approach of directly learning deep embeddings for brain tumor types, which can be used for downstream tasks such as classification. We evaluate our method on an extensive brain tumor dataset which consists of 27 different tumor classes, out of which 13 are defined as rare.
  arXiv  Detail & Related papers  (2021-08-08T11:26:34Z)
• Glioma Prognosis: Segmentation of the Tumor and Survival Prediction using Shape, Geometric and Clinical Information [13.822139791199106]
  We exploit a convolutional neural network (CNN) with hypercolumn technique to segment tumor from healthy brain tissue. Our model achieves a mean dice accuracy of 87.315%, 77.04% and 70.22% for the whole tumor, tumor core and enhancing tumor respectively.
  arXiv  Detail & Related papers  (2021-04-02T10:49:05Z)
• Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans [5.586191108738564]
  Gliomas are one of the most frequent brain tumors and are classified into high grade and low grade gliomas. Here, we have developed a multi-threshold model based on attention U-Net for identification of various regions of the tumor in magnetic resonance imaging (MRI) The proposed model achieved mean Dice Coefficient of 0.59, 0.72, and 0.61 for enhancing tumor, whole tumor and tumor core respectively on the training dataset. The same model gave mean Dice Coefficient of 0.57, 0.73, and 0.61 on the validation dataset and 0.59, 0.72, and 0.57 on the test dataset.
  arXiv  Detail & Related papers  (2021-01-29T04:53:42Z)
• Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution [56.17099252139182]
  We automate and standardize the task of brain tumor segmentation with U-net like neural networks. Two independent ensembles of models were trained, and each produced a brain tumor segmentation map. Our solution achieved a Dice of 0.79, 0.89 and 0.84, as well as Hausdorff 95% of 20.4, 6.7 and 19.5mm on the final test dataset.
  arXiv  Detail & Related papers  (2020-10-30T14:36:10Z)

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.