Extending nn-UNet for brain tumor segmentation

Luu, Huan Minh; Park, Sung-Hong

doi:10.48550/arxiv.2112.04653

Cited by 9 publications

(10 citation statements)

References 24 publications

(33 reference statements)

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“…Although the efficacy of nnU-Net, which scored the highest in the Multimodal Brain Tumor Segmentation (BraTS) Challenge 2021, is well known, its applicability for meningioma has yet to be published. 33 Thanks to the advantages of nnU-Net in analyzing medical images composed of heterogeneous datasets through self-configuration, we were able to train the models using a training set that partially (17%) consisted of external images without producing noticeable performance degradation as a result of the heterogeneity. 12 This training with a heterogeneous dataset is likely why the performance with the EVS does not fall significantly than with the IVS, unlike what has been observed in previous studies.…”

Section: Discussionmentioning

confidence: 99%

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning

Kang

Witanto²,

Pratama³

et al. 2022

Magnetic Resonance Imaging

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Background Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated volumetric tools limit their use in routine clinical practice. Purpose To develop and validate a computational model for fully automated meningioma segmentation and volume measurement on contrast‐enhanced MRI scans using deep learning. Study Type Retrospective. Population A total of 659 intracranial meningioma patients (median age, 59.0 years; interquartile range: 53.0–66.0 years) including 554 women and 105 men. Field Strength/Sequence The 1.0 T, 1.5 T, and 3.0 T; three‐dimensional, T1‐weighted gradient‐echo imaging with contrast enhancement. Assessment The tumors were manually segmented by two neurosurgeons, H.K. and C.‐K.P., with 10 and 26 years of clinical experience, respectively, for use as the ground truth. Deep learning models based on U‐Net and nnU‐Net were trained using 459 subjects and tested for 100 patients from a single institution (internal validation set [IVS]) and 100 patients from other 24 institutions (external validation set [EVS]), respectively. The performance of each model was evaluated with the Sørensen‐Dice similarity coefficient (DSC) compared with the ground truth. Statistical Tests According to the normality of the data distribution verified by the Shapiro–Wilk test, variables with three or more categories were compared by the Kruskal–Wallis test with Dunn's post hoc analysis. Results A two‐dimensional (2D) nnU‐Net showed the highest median DSCs of 0.922 and 0.893 for the IVS and EVS, respectively. The nnU‐Nets achieved superior performance in meningioma segmentation than the U‐Nets. The DSCs of the 2D nnU‐Net for small meningiomas less than 1 cm3 were 0.769 and 0.780 with the IVS and EVS, respectively. Data Conclusion A fully automated and accurate volumetric measurement tool for meningioma with clinically applicable performance for small meningioma using nnU‐Net was developed. Evidence Level 3 Technical Efficacy Stage 2

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Section: Discussionmentioning

confidence: 99%

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning

Kang

Witanto²,

Pratama³

et al. 2022

Magnetic Resonance Imaging

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“…Currently, more and more 3D BTS models are proposed to leverage 3D spatial information. nnU-Net [18] is a general and adaptive baseline model for both 2D and 3D medical image segmentation, which derives a series of nnU-Net-based BTS models [19], [20]. Liu et al [21] propose CANet to capture the sequential information by introducing feature interaction graphs.…”

Section: A Cnn-based Bts Modelsmentioning

confidence: 99%

CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer With Modality-Correlated Cross-Attention for Brain Tumor Segmentation

Lin

et al. 2023

IEEE Trans. Med. Imaging

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Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial for brain tumor diagnosis, cancer management and research purposes. With the great success of the ten-year BraTS challenges as well as the advances of CNN and Transformer algorithms, a lot of outstanding BTS models have been proposed to tackle the difficulties of BTS in different technical aspects. However, existing studies hardly consider how to fuse the multimodality images in a reasonable manner. In this paper, we leverage the clinical knowledge of how radiologists diagnose brain tumors from multiple MRI modalities and propose a clinical knowledge-driven brain tumor segmentation model, called CKD-TransBTS. Instead of directly concatenating all the modalities, we re-organize the input modalities by separating them into two groups according to the imaging principle of MRI. A dual-branch hybrid encoder with the proposed modality-correlated crossattention block (MCCA) is designed to extract the multimodality image features. The proposed model inherits the strengths from both Transformer and CNN with the local feature representation ability for precise lesion boundaries and long-range feature extraction for 3D volumetric images. To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC)

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“…There are many other U-Net variants such as Optimized U-Net [35] and nnU-Net ( No New U-Net ) [29] for brain tumour segmentation, Swin U-Net [34] for medical image segmentation, D-UNet ( Dimension fusion U-Net ) [15] for chronic stroke lesion segmentation, and many others.…”

Section: Background and Related Workmentioning

confidence: 99%

Brain Tumour Segmentation Using S-Net and SA-Net

et al. 2023

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Image segmentation is an application area of computer vision and digital image processing that partitions a digital image into multiple image regions or segments. This process involves the extraction of a set of contours from the input digital image in such a manner that pixels belonging to a region share some common characteristics or computed properties, such as color, texture, or intensity. The application domain of image segmentation is widespread, and includes video surveillance, object detection, traffic control systems, and medical imaging. The application of image segmentation techniques in the field of medical imaging can be further subcategorized into virtual surgery simulation, diagnosis, study of anatomical structures, measurement of tissue volumes, location of tumors and other pathologies. In this study, we have proposed two new Convolutional Neural Network (CNN)-based models: (a) S-Net and (b) Attention S-Net (SA-Net) to perform image segmentation tasks in the field of medical imaging, especially to generate segmentation masks for brain tumours if present in brain Medical Resonance Imaging (MRI). Both proposed models were developed by considering U-Net as the base architecture. The newly proposed models have leveraged the concept of 'Merge Block' to infuse both the local and global context, and 'Attention Block' to focus on the region of interest having a specific object. Additionally, it uses techniques, such as data augmentation to utilize the available annotated samples more efficiently. The proposed models achieved a Dice Similarity Coefficient (DSC) measure of 0.78 and 0.80 for the High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) datasets, respectively.

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Extending nn-UNet for brain tumor segmentation

Cited by 9 publications

References 24 publications

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning

CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer With Modality-Correlated Cross-Attention for Brain Tumor Segmentation

Brain Tumour Segmentation Using S-Net and SA-Net

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