Fully automated liver segmentation using Sobolev gradient‐based level set evolution

Göçeri, Evgin

doi:10.1002/cnm.2765

Cited by 39 publications

(11 citation statements)

References 59 publications

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“…For CT, state-of-the-art algorithms typically have shown good performance, with mean Dice scores of 0.93-0.95 (13)(14)(15). For MRI, a wide performance range has been reported, with mean Dice scores ranging from 0.85 to 0.95 depending on the specific MR technique and contrast material used (9,(16)(17)(18)(19)34,35). In this work, we explored the feasibility of a single multimodal CNN to perform this task with comparable accuracy across different imaging modalities and techniques.…”

Section: Discussionmentioning

confidence: 99%

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network

Wang¹,

Mamidipalli²,

Retson³

et al. 2019

Radiology: Artificial Intelligence

105

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To assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and to apply this technique to enable automation of liver biometry. Materials and Methods: A two-dimensional U-Net CNN was trained for liver segmentation in two stages by using 330 abdominal MRI and CT examinations. First, the neural network was trained with unenhanced multiecho spoiled gradient-echo images from 300 MRI examinations to yield multiple signal weightings. Then, transfer learning was used to generalize the CNN with additional images from 30 contrast material-enhanced MRI and CT examinations. Performance of the CNN was assessed by using a distinct multiinstitutional dataset curated from multiple sources (498 subjects). Segmentation accuracy was evaluated by computing Dice scores. These segmentations were used to compute liver volume from CT and T1-weighted MRI examinations and to estimate hepatic proton density fat fraction (PDFF) from multiecho T2*-weighted MRI examinations. Quantitative volumetry and PDFF estimates were compared between automated and manual segmentation by using Pearson correlation and Bland-Altman statistics. Results: Dice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1-weighted MRI, and 0.92 ± 0.05 for T2*weighted MRI (n = 168). Liver volume measured with manual and automated segmentation agreed closely for CT (95% limits of agreement: −298 mL, 180 mL) and T1-weighted MRI (95% limits of agreement: −358 mL, 180 mL). Hepatic PDFF measured by the two segmentations also agreed closely (95% limits of agreement: −0.62%, 0.80%). Conclusion: By using a transfer-learning strategy, this study has demonstrated the feasibility of a CNN to be generalized to perform liver segmentation across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization.

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Section: Discussionmentioning

confidence: 99%

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network

Wang¹,

Mamidipalli²,

Retson³

et al. 2019

Radiology: Artificial Intelligence

105

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“…This level set approach has fast computational time (a large advantage for this application), robustness to image noise and missing edges (commonly encountered in MRI), and flexibility by modifying or choosing just a few parameters. Broadly, due to these desirable properties, level sets have been demonstrated as being superior compared to other methods for segmenting various tissues on MR images such as brain tumors as well as whole organs such as the liver [12, 13, 33, 35]. The level set is described by…”

Section: Methodsmentioning

confidence: 99%

WRIST: A WRist Image Segmentation Toolkit for carpal bone delineation from MRI

Foster

Joshi

Borgese

et al. 2018

Computerized Medical Imaging and Graphics

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Segmentation of the carpal bones from 3D imaging modalities, such as magnetic resonance imaging (MRI), is commonly performed for in vivo analysis of wrist morphology, kinematics, and biomechanics. This crucial task is typically carried out manually and is labor intensive, time consuming, subject to high inter- and intra-observer variability, and may result in topologically incorrect surfaces. We present a method, WRist Image Segmentation Toolkit (WRIST), for 3D semi-automated, rapid segmentation of the carpal bones of the wrist from MRI. In our method, the boundary of the bones were iteratively found using prior known anatomical constraints and a shape-detection level set. The parameters of the method were optimized using a training dataset of 48 manually segmented carpal bones and evaluated on 112 carpal bones which included both healthy participants without known wrist conditions and participants with thumb basilar osteoarthritis (OA). Manual segmentation by two expert human observers was considered as a reference. On the healthy subject dataset we obtained a Dice overlap of 93.0 ± 3.8, Jaccard Index of 87.3 ± 6.2, and a Hausdorff distance of 2.7 ± 3.4 mm, while on the OA dataset we obtained a Dice overlap of 90.7 ± 8.6, Jaccard Index of 83.0 ± 10.6, and a Hausdorff distance of 4.0 ± 4.4 mm. The short computational time of 20.8 s per bone (or 5.1 s per bone in the parallelized version) and the high agreement with the expert observers gives WRIST the potential to be utilized in musculoskeletal research.

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“…82,83 (Sobolev gradient computations are not given here to avoid making the length of the paper too long because of unnecessary information. The readers can see [81][82][83] for details). Algorithm #1 presents the proposed optimization that is called as Sobolev gradient-based Accelerated Stochastic Gradient Descent (SASGradD).…”

Section: Optimization In the 3d Cnnmentioning

confidence: 99%

Diagnosis of Alzheimer's disease with Sobolev gradient‐based optimization and 3D convolutional neural network

Göçeri

2019

Numer Methods Biomed Eng

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Alzheimer's disease is a neuropsychiatric, progressive, also an irreversible disease. There is not an effective cure for the disease. However, early diagnosis has an important role for treatment planning to delay its progression since the treatments have the most impact at the early stage of the disease. Neuroimages obtained by different imaging techniques (for example, diffusion tensor‐based and magnetic resonance‐based imaging) provide powerful information and help to diagnose the disease. In this work, a deeply supervised and robust method has been developed using three dimensional features to provide objective and accurate diagnosis from magnetic resonance images. The main contributions are (a) a new three dimensional convolutional neural network topology; (b) a new Sobolev gradient‐based optimization with weight values for each decision parameters; (c) application of the proposed topology and optimizer to diagnose Alzheimer's disease; (d) comparisons of the results obtained from the recent techniques that have been implemented for Alzheimer's disease diagnosis. Experimental results and quantitative evaluations indicated that the proposed network model is able to achieve to extract desired features from images and provides automated diagnosis with 98.06% accuracy.

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Fully automated liver segmentation using Sobolev gradient‐based level set evolution

Cited by 39 publications

References 59 publications

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network

WRIST: A WRist Image Segmentation Toolkit for carpal bone delineation from MRI

Diagnosis of Alzheimer's disease with Sobolev gradient‐based optimization and 3D convolutional neural network

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