Liver Segmentation in Magnetic Resonance Imaging via Mean Shape Fitting with Fully Convolutional Neural Networks

Zeng, Qi; Karimi, Davood; Pang, Emily; Mohammed, Shahed K.; Schneider, Charles L.; Honarvar, Mohammad; Salcudean, Septimiu E.

doi:10.1007/978-3-030-32245-8_28

Cited by 20 publications

(12 citation statements)

References 12 publications

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“…Moreover, our results for segmentation of liver and spleen in the reference MRI experiment were slightly lower than results reported by other research groups (e.g. [6,16,17]). While this may also be caused by the small data set, it indicates room for improvement regarding our architecture.…”

Section: Discussioncontrasting

confidence: 90%

“…To this end, we investigated an approach to segment three organs (liver, spleen and spine) directly in PET images utilizing convolutional neural networks (CNNs). These organs have been chosen because liver and spleen, on the one hand, have been segmented successfully in MR and CT images [6,16,17]. The spine, on the other hand, is a rather complex organ composed of many small vertebrae, making it a more challenging task.…”

Section: Introductionmentioning

confidence: 99%

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Automated Multi-Organ Segmentation in Pet Images Using Cascaded Training of a 3d U-Net and Convolutional Autoencoder

Liebgott

Lorenz

Gatidis

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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PET imaging is an important tool in clinical diagnostics, especially in oncology as it is able to visualize ongoing metabolic processes, e.g. caused by a tumor. Due to the low spatial resolution, a corresponding CT or MRI scan is normally necessary to gain knowledge about the physiological structures of a patient and especially to perform some computer-aided diagnostics methods, e.g. segmentation of structures of interest. As this transfer of information from CT/MRI to the PET domain is not always feasible, e.g. when the corresponding CT or MRI images are unavailable or corrupted by artifacts, we propose a novel approach to perform organ segmentation on the PET images directly. We utilize a CNN architecture based on a 3D U-Net combined with a convolutional autoencoder and train our model purely on PET images and corresponding ground truth masks. Our resulting Dice scores of 0.88, 0.82 an 0.59 for liver, spleen and spine, respectively, show that standalone PET organ segmentation is generally feasible.

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Section: Discussioncontrasting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Automated Multi-Organ Segmentation in Pet Images Using Cascaded Training of a 3d U-Net and Convolutional Autoencoder

Liebgott

Lorenz

Gatidis

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Many current state-of-the-art deep learning segmentation algorithms use encoder-decoder network architectures, and many practical improvements in segmentation performance can be realized through innovations in pre-processing [ 51 ], data augmentation and loss functions [ 29 ]. Previous liver segmentation studies have used the U-net architecture [ 19 , 29 , 32 , 36 , 37 , 39 ] or its variants [ 34 , 38 ]. The method of Bousabarah et al [ 19 ] trained on 121 triphasic MR scans and tested on a set of 26 patients yielded a mean DSC of 0.91 (±0.01).…”

Section: Discussionmentioning

confidence: 99%

“…The method of Bousabarah et al [ 19 ] trained on 121 triphasic MR scans and tested on a set of 26 patients yielded a mean DSC of 0.91 (±0.01). The proposed fully convolutional neural network of Zeng et al [ 34 ] used T2-weighted MR images and showed a DSC (mean±SD) of 0.952±0.01 on 51 validation patients. Wang et al’s 2D U-net CNN for liver segmentation yielded a mean DSC of 0.95±0.03 with their method trained using 330 MRI and CT scans and tested on 100 T1-weighted MR images [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging

et al. 2021

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Purpose Accurate liver segmentation is key for volumetry assessment to guide treatment decisions. Moreover, it is an important pre-processing step for cancer detection algorithms. Liver segmentation can be especially challenging in patients with cancer-related tissue changes and shape deformation. The aim of this study was to assess the ability of state-of-the-art deep learning 3D liver segmentation algorithms to generalize across all different Barcelona Clinic Liver Cancer (BCLC) liver cancer stages. Methods This retrospective study, included patients from an institutional database that had arterial-phase T1-weighted magnetic resonance images with corresponding manual liver segmentations. The data was split into 70/15/15% for training/validation/testing each proportionally equal across BCLC stages. Two 3D convolutional neural networks were trained using identical U-net-derived architectures with equal sized training datasets: one spanning all BCLC stages (“All-Stage-Net": AS-Net), and one limited to early and intermediate BCLC stages (“Early-Intermediate-Stage-Net": EIS-Net). Segmentation accuracy was evaluated by the Dice Similarity Coefficient (DSC) on a dataset spanning all BCLC stages and a Wilcoxon signed-rank test was used for pairwise comparisons. Results 219 subjects met the inclusion criteria (170 males, 49 females, 62.8±9.1 years) from all BCLC stages. Both networks were trained using 129 subjects: AS-Net training comprised 19, 74, 18, 8, and 10 BCLC 0, A, B, C, and D patients, respectively; EIS-Net training comprised 21, 86, and 22 BCLC 0, A, and B patients, respectively. DSCs (mean±SD) were 0.954±0.018 and 0.946±0.032 for AS-Net and EIS-Net (p<0.001), respectively. The AS-Net 0.956±0.014 significantly outperformed the EIS-Net 0.941±0.038 on advanced BCLC stages (p<0.001) and yielded similarly good segmentation performance on early and intermediate stages (AS-Net: 0.952±0.021; EIS-Net: 0.949±0.027; p = 0.107). Conclusion To ensure robust segmentation performance across cancer stages that is independent of liver shape deformation and tumor burden, it is critical to train deep learning models on heterogeneous imaging data spanning all BCLC stages.

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“…Semantic segmentation of medical images is regularly performed using deep convolutional neural networks (CNNs), such as the U-Net [15]. One alternative segmentation method uses a combination of CNNs and spatial transformers to learn the spatial warping required to transform a set of prior shapes into the desired class labels [10,18,17,8,19]. Such methods have outperformed conventional encoder-decoder and state-of-the-art architectures, however, they have no topological guarantees.…”

Section: Introductionmentioning

confidence: 99%

TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations

Wyburd¹,

Dinsdale²,

Namburete³

et al. 2021

Preprint

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Accurate topology is key when performing meaningful anatomical segmentations, however, it is often overlooked in traditional deep learning methods. In this work we propose TEDS-Net: a novel segmentation method that guarantees accurate topology. Our method is built upon a continuous diffeomorphic framework, which enforces topology preservation. However, in practice, diffeomorphic fields are represented using a finite number of parameters and sampled using methods such as linear interpolation, violating the theoretical guarantees. We therefore introduce additional modifications to more strictly enforce it. Our network learns how to warp a binary prior, with the desired topological characteristics, to complete the segmentation task. We tested our method on myocardium segmentation from an open-source 2D heart dataset. TEDS-Net

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Liver Segmentation in Magnetic Resonance Imaging via Mean Shape Fitting with Fully Convolutional Neural Networks

Cited by 20 publications

References 12 publications

Automated Multi-Organ Segmentation in Pet Images Using Cascaded Training of a 3d U-Net and Convolutional Autoencoder

Automated Multi-Organ Segmentation in Pet Images Using Cascaded Training of a 3d U-Net and Convolutional Autoencoder

Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging

TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations

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