Meningioma segmentation in T1-weighted MRI leveraging global context and
attention mechanisms
- URL: http://arxiv.org/abs/2101.07715v1
- Date: Tue, 19 Jan 2021 16:40:53 GMT
- Title: Meningioma segmentation in T1-weighted MRI leveraging global context and
attention mechanisms
- Authors: David Bouget, Andr\'e Pedersen, Sayied Abdol Mohieb Hosainey, Ole
Solheim, Ingerid Reinertsen
- Abstract summary: Meningiomas are the most common type of primary brain tumor, accounting for approximately 30% of all brain tumors.
We propose the inclusion of attention mechanisms over a U-Net architecture.
We studied the impact of multi-scale input and deep supervision components.
- Score: 0.2624902795082451
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Meningiomas are the most common type of primary brain tumor, accounting for
approximately 30% of all brain tumors. A substantial number of these tumors are
never surgically removed but rather monitored over time. Automatic and precise
meningioma segmentation is therefore beneficial to enable reliable growth
estimation and patient-specific treatment planning. In this study, we propose
the inclusion of attention mechanisms over a U-Net architecture: (i)
Attention-gated U-Net (AGUNet) and (ii) Dual Attention U-Net (DAUNet), using a
3D MRI volume as input. Attention has the potential to leverage the global
context and identify features' relationships across the entire volume. To limit
spatial resolution degradation and loss of detail inherent to encoder-decoder
architectures, we studied the impact of multi-scale input and deep supervision
components. The proposed architectures are trainable end-to-end and each
concept can be seamlessly disabled for ablation studies. The validation studies
were performed using a 5-fold cross validation over 600 T1-weighted MRI volumes
from St. Olavs University Hospital, Trondheim, Norway. For the best performing
architecture, an average Dice score of 81.6% was reached for an F1-score of
95.6%. With an almost perfect precision of 98%, meningiomas smaller than 3ml
were occasionally missed hence reaching an overall recall of 93%. Leveraging
global context from a 3D MRI volume provided the best performances, even if the
native volume resolution could not be processed directly. Overall, near-perfect
detection was achieved for meningiomas larger than 3ml which is relevant for
clinical use. In the future, the use of multi-scale designs and refinement
networks should be further investigated to improve the performance. A larger
number of cases with meningiomas below 3ml might also be needed to improve the
performance for the smallest tumors.
Related papers
- LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation [7.1789008189318455]
Lightweight 3D ATtention U-Net with Parallel convolutions, LATUP-Net, is designed to reduce computational requirements significantly while maintaining high segmentation performance.
LATUP-Net achieves promising segmentation performance: the average Dice scores for the whole tumor, tumor core, and enhancing tumor on the BraTS2020 dataset are 88.41%, 83.82%, and 73.67%, and on the BraTS2021 dataset, they are 90.29%, 89.54%, and 83.92%, respectively.
arXiv Detail & Related papers (2024-04-09T00:05:45Z) - Weakly supervised segmentation of intracranial aneurysms using a novel 3D focal modulation UNet [0.5106162890866905]
We propose FocalSegNet, a novel 3D focal modulation UNet, to detect an aneurysm and offer an initial, coarse segmentation of it from time-of-flight MRA image patches.
We trained and evaluated our model on a public dataset, and in terms of UIA detection, our model showed a low false-positive rate of 0.21 and a high sensitivity of 0.80.
arXiv Detail & Related papers (2023-08-06T03:28:08Z) - 3D Medical Image Segmentation based on multi-scale MPU-Net [5.393743755706745]
This paper proposes a tumor segmentation model MPU-Net for patient volume CT images.
It is inspired by Transformer with a global attention mechanism.
Compared with the benchmark model U-Net, MPU-Net shows excellent segmentation results.
arXiv Detail & Related papers (2023-07-11T20:46:19Z) - Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net [0.0]
This paper proposes a 3D attention-based U-Net architecture for multi-region segmentation of brain tumors.
The attention mechanism helps to improve segmentation accuracy by de-emphasizing healthy tissues and accentuating malignant tissues.
arXiv Detail & Related papers (2023-05-10T14:35:07Z) - 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) - CNN-based fully automatic wrist cartilage volume quantification in MR
Image [55.41644538483948]
The U-net convolutional neural network with additional attention layers provides the best wrist cartilage segmentation performance.
The error of cartilage volume measurement should be assessed independently using a non-MRI method.
arXiv Detail & Related papers (2022-06-22T14:19:06Z) - Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents.
We generate an automatic tumor boundary detector for the rare disease of glioblastoma.
We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
arXiv Detail & Related papers (2022-04-22T17:27:00Z) - A unified 3D framework for Organs at Risk Localization and Segmentation
for Radiation Therapy Planning [56.52933974838905]
Current medical workflow requires manual delineation of organs-at-risk (OAR)
In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation.
Our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging.
arXiv Detail & Related papers (2022-03-01T17:08:41Z) - 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) - Fast meningioma segmentation in T1-weighted MRI volumes using a
lightweight 3D deep learning architecture [0.19573380763700712]
We optimized the segmentation and processing speed performances using a large number of surgically treated meningiomas and untreated meningiomas.
We studied two different 3D neural network architectures: (i) a simple encoder-decoder similar to a 3D U-Net, and (ii) a lightweight multi-scale architecture (PLS-Net)
The models were evaluated in terms of detection accuracy, segmentation accuracy and training/inference speed.
arXiv Detail & Related papers (2020-10-14T12:26:53Z) - 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.