Optimized U-Net for Brain Tumor Segmentation

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

doi:10.1007/978-3-031-09002-8_2

Cited by 48 publications

(24 citation statements)

References 20 publications

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“…For the U-Net, different optimization techniques could be explored. For example, broad ablation studies could be performed; exploration of the number of layers, epochs, optimizer, loss function, residual connections or decoder attention [ 88 , 89 , 90 ]. The modification or addition of layers is also a possibility, for instance, interpolation and convolution instead of transposed convolution [ 91 ], adding deconvolution and upsampling layers in the splicing process [ 92 ], or fuzzy layers in addition to the conventional layers [ 93 ].…”

Section: Discussionmentioning

confidence: 99%

Impact of Training Data, Ground Truth and Shape Variability in the Deep Learning-Based Semantic Segmentation of HeLa Cells Observed with Electron Microscopy

2023

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This paper investigates the impact of the amount of training data and the shape variability on the segmentation provided by the deep learning architecture U-Net. Further, the correctness of ground truth (GT) was also evaluated. The input data consisted of a three-dimensional set of images of HeLa cells observed with an electron microscope with dimensions 8192×8192×517. From there, a smaller region of interest (ROI) of 2000×2000×300 was cropped and manually delineated to obtain the ground truth necessary for a quantitative evaluation. A qualitative evaluation was performed on the 8192×8192 slices due to the lack of ground truth. Pairs of patches of data and labels for the classes nucleus, nuclear envelope, cell and background were generated to train U-Net architectures from scratch. Several training strategies were followed, and the results were compared against a traditional image processing algorithm. The correctness of GT, that is, the inclusion of one or more nuclei within the region of interest was also evaluated. The impact of the extent of training data was evaluated by comparing results from 36,000 pairs of data and label patches extracted from the odd slices in the central region, to 135,000 patches obtained from every other slice in the set. Then, 135,000 patches from several cells from the 8192×8192 slices were generated automatically using the image processing algorithm. Finally, the two sets of 135,000 pairs were combined to train once more with 270,000 pairs. As would be expected, the accuracy and Jaccard similarity index improved as the number of pairs increased for the ROI. This was also observed qualitatively for the 8192×8192 slices. When the 8192×8192 slices were segmented with U-Nets trained with 135,000 pairs, the architecture trained with automatically generated pairs provided better results than the architecture trained with the pairs from the manually segmented ground truths. This suggests that the pairs that were extracted automatically from many cells provided a better representation of the four classes of the various cells in the 8192×8192 slice than those pairs that were manually segmented from a single cell. Finally, the two sets of 135,000 pairs were combined, and the U-Net trained with these provided the best results.

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Section: Discussionmentioning

confidence: 99%

Impact of Training Data, Ground Truth and Shape Variability in the Deep Learning-Based Semantic Segmentation of HeLa Cells Observed with Electron Microscopy

2023

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“…We used nnUnet ( Futrega et al, 2022 ), a model derived from Unet ( Ronneberger et al, 2015 ) and tuned to segment brain tumors in MRI scans. This model demonstrates state-of-the-art results on pre-operative GBM segmentation tasks ( Menze et al, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we compared five models that were trained on the post-operative GBM training set: 1.a baseline nnUnet network ( Futrega et al, 2022 ) trained on three labels: (a) resection; (b) necrotic core, and (c) enhancing; 2. the baseline nnUnet network including surgical information as a post-process; 3. using the model in 2, and segmenting CSF as an additional label; 4. our full model with two baseline nnUnet networks incorporating the surgical type, the CSF and uncertainty regularization as described above, and 5. our full model as in 4, but incorporating a user defined certainty threshold. To this end, we developed a graphical user interface in 3D Slicer ( Fedorov et al, 2012 ) that allows the physician to choose the level of certainty in each case separately.…”

Section: Methodsmentioning

confidence: 99%

Post-operative glioblastoma multiforme segmentation with uncertainty estimation

Gazit¹,

Faran²,

Stepovoy³

et al. 2022

Front. Hum. Neurosci.

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Segmentation of post-operative glioblastoma multiforme (GBM) is essential for the planning of Tumor Treating Fields (TTFields) treatment and other clinical applications. Recent methods developed for pre-operative GBM segmentation perform poorly on post-operative GBM MRI scans. In this paper we present a method for the segmentation of GBM in post-operative patients. Our method incorporates an ensemble of segmentation networks and the Kullback–Leibler divergence agreement score in the objective function to estimate the prediction label uncertainty and cope with noisy labels and inter-observer variability. Moreover, our method integrates the surgery type and computes non-tumorous tissue delineation to automatically segment the tumor. We trained and validated our method on a dataset of 340 enhanced T1 MRI scans of patients that were treated with TTFields (270 scans for train and 70 scans for test). For validation, we developed a tool that uses the uncertainty map along with the segmentation result. Our tool allows visualization and fast editing of the tissues to improve the results dependent on user preference. Three physicians reviewed and graded our segmentation and editing tool on 12 different MRI scans. The validation set average (SD) Dice scores were 0.81 (0.11), 0.71 (0.24), 0.64 (0.25), and 0.68 (0.19) for whole-tumor, resection, necrotic-core, and enhancing-tissue, respectively. The physicians rated 72% of the segmented GBMs acceptable for treatment planning or better. Another 22% can be edited manually in a reasonable time to achieve a clinically acceptable result. According to these results, the proposed method for GBM segmentation can be integrated into TTFields treatment planning software in order to shorten the planning process. To conclude, we have extended a state-of-the-art pre-operative GBM segmentation method with surgery-type, anatomical information, and uncertainty visualization to facilitate a clinically viable segmentation of post-operative GBM for TTFields treatment planning.

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“…Since 2014, deep learning algorithms have been extensively researched for tumor segmentation in the BraTS challenge [9,[30][31][32][33][34][35][36][37][38][39]. Myronenko [32] won the BraTS 2018 competition by training an asymmetrical U-Net with a broader encoder and an additional variational decoder branch that provided further regularization.…”

Section: Tumor Segmentationmentioning

confidence: 99%

“…A recent study [37] proposed an optimized U-Net architecture for the BraTS challenge. To find the optimal architecture and learning strategy, extensive ablation studies were conducted to test: U-Net depth, number of convolutional channels, decoder attention, residual connections, losses, and post-processing strategy.…”

Section: Tumor Segmentationmentioning

confidence: 99%

Large-Kernel Attention for 3D Medical Image Segmentation

Nan

Ser

et al. 2023

Cogn Comput

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Automated segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In addition, the diversity of tumor shape, location, and appearance, coupled with the dominance of background voxels, makes accurate 3D medical image segmentation difficult. In this paper, a novel 3D large-kernel (LK) attention module is proposed to address these problems to achieve accurate multi-organ segmentation and tumor segmentation. The advantages of biologically inspired self-attention and convolution are combined in the proposed LK attention module, including local contextual information, long-range dependencies, and channel adaptation. The module also decomposes the LK convolution to optimize the computational cost and can be easily incorporated into CNNs such as U-Net. Comprehensive ablation experiments demonstrated the feasibility of convolutional decomposition and explored the most efficient and effective network design. Among them, the best Mid-type 3D LK attention-based U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving state-of-the-art segmentation performance when compared to avant-garde CNN and Transformer-based methods for medical image segmentation. The performance improvement due to the proposed 3D LK attention module was statistically validated.

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Optimized U-Net for Brain Tumor Segmentation

Cited by 48 publications

References 20 publications

Impact of Training Data, Ground Truth and Shape Variability in the Deep Learning-Based Semantic Segmentation of HeLa Cells Observed with Electron Microscopy

Impact of Training Data, Ground Truth and Shape Variability in the Deep Learning-Based Semantic Segmentation of HeLa Cells Observed with Electron Microscopy

Post-operative glioblastoma multiforme segmentation with uncertainty estimation

Large-Kernel Attention for 3D Medical Image Segmentation

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