Optimized U-Net for Brain Tumor Segmentation

Michał, Futrega,; Milesi, Alexandre; Marcinkiewicz, M.; Ribalta, Pablo

doi:10.48550/arxiv.2110.03352

Cited by 7 publications

(4 citation statements)

References 13 publications

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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%

“…We compare our proposed CKD-TransBTS model with several SOTA models, including six CNN-based models (VNet [45], ResUNet [46], LSTM-CNN [47], UNet++ [48] AttentionUNet [49] and DynUNet [20]) and six transformerbased models (TransBTS [28], TransUNet [27], UNETR [7], VTNet [30], SegTransVAE [50] and Swin UNETR [32]). For each baseline model in this experiment, we directly run the code if it has been released.…”

Section: B Comparisons With Sota Modelsmentioning

confidence: 99%

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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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Section: A Cnn-based Bts Modelsmentioning

confidence: 99%

Section: B Comparisons With Sota 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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“…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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“…Our tissue detection solution utilizes a convolutional neural network model with a standard U-Net architecture [29], which is configured as follows (Figure 8): it comprises an input layer, succeeded by 6 encoder blocks. Each encoder block consists of two convolution blocks and a max-pooling layer, with batch normalization and ReLU activation for feature extraction.…”

Section: Training Process With Overlay Augmentationmentioning

confidence: 99%

Artifact Augmentation for Enhanced Tissue Detection in Microscope Scanner Systems

Küttel,

Kovács,

Szölgyén

et al. 2023

Sensors

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As the field of routine pathology transitions into the digital realm, there is a surging demand for the full automation of microscope scanners, aiming to expedite the process of digitizing tissue samples, and consequently, enhancing the efficiency of case diagnoses. The key to achieving seamless automatic imaging lies in the precise detection and segmentation of tissue sample regions on the glass slides. State-of-the-art approaches for this task lean heavily on deep learning techniques, particularly U-Net convolutional neural networks. However, since samples can be highly diverse and prepared in various ways, it is almost impossible to be fully prepared for and cover every scenario with training data. We propose a data augmentation step that allows artificially modifying the training data by extending some artifact features of the available data to the rest of the dataset. This procedure can be used to generate images that can be considered synthetic. These artifacts could include felt pen markings, speckles of dirt, residual bubbles in covering glue, or stains. The proposed approach achieved a 1–6% improvement for these samples according to the F1 Score metric.

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Optimized U-Net for Brain Tumor Segmentation

Cited by 7 publications

References 13 publications

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

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

Artifact Augmentation for Enhanced Tissue Detection in Microscope Scanner Systems

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