Joint Segmentation of Image Ensembles via Latent Atlases

Raviv, Tammy Riklin; Leemput, Koen Van; Wells, William M.; Golland, Polina

doi:10.1007/978-3-642-04268-3_34

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…This paper extends the work previously presented in (Riklin Raviv et al, 2009b,a) by providing detailed derivations of the underlying mathematical model and thorough experimental validation and implementation details.…”

Section: Introductionsupporting

confidence: 53%

Segmentation of image ensembles via latent atlases

Riklin-Raviv

Leemput

Menze

et al. 2010

Medical Image Analysis

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Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. However, the availability of comprehensive, reliable and suitable manual segmentations for atlas construction is limited. We therefore propose a method for joint segmentation of corresponding regions of interest in a collection of aligned images that does not require labeled training data. Instead, a latent atlas, initialized by at most a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The algorithm is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method on two datasets, segmenting subcortical and cortical structures in a multi-subject study and extracting brain tumors in a single-subject multi-modal longitudinal experiment. We compare the segmentation results to manual segmentations, when those exist, and to the results of a state-of-the-art atlas-based segmentation method. The quality of the results supports the latent atlas as a promising alternative when existing atlases are not compatible with the images to be segmented.

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Section: Introductionsupporting

confidence: 53%

Segmentation of image ensembles via latent atlases

Riklin-Raviv

Leemput

Menze

et al. 2010

Medical Image Analysis

Self Cite

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“…The main components of semi-automatic brain tumor segmentation include user interaction and automatic software computation. Users are required to analyze the visual information and provide initial delineation inputs and feedback for the software to perform segmentation (Raviv et al 2009, Riklin-Raviv et al 2010, Fitton 2011, Hamamci et al 2012, Guo et al 2013, Havaei 2014, Cui et al 2016. The feedback is subjective and prone to intra-and inters-observer variation.…”

Section: Introductionmentioning

confidence: 99%

Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications

Stojadinović

Hrycushko

et al. 2016

Phys. Med. Biol.

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The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98 ± 0.01, an NMI of 0.97 ± 0.01, an SSIM of 0.999 ± 0.001, an HD of 2.2 ± 0.8 mm, an MSSD of 0.1 ± 0.1 mm, and an SDSSD of 0.3 ± 0.1 mm. The validation on the BRATS data resulted in a DC of 0.89 ± 0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86 ± 0.09, an NMI of 0.80 ± 0.11, an SSIM of 0.999 ± 0.001, an HD of 8.8 ± 12.6 mm, an MSSD of 1.5 ± 3.2 mm, and an SDSSD of 1.8 ± 3.4 mm when comparing to the physician drawn ground truth. The result indicated that the developed automatic segmentation strategy yielded accurate brain tumor delineation and presented as a useful clinical tool for SRS applications.

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Brain tumor segmentation using holistically nested neural networks in MRI images

et al. 2017

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Purpose Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic Resonance Imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 years of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically-nested neural networks that could be employed for brain tumor segmentation of MRI images. Methods Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically-nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multi-scale and multi-level hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. Results The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training data sets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training data sets with High Grade Gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to 10 clinical data sets with HGG from a locally developed database. DSC and sensitivity of 0.83 and 0.85 were achieved. A quantitative comparison indicated that the proposed method outperforms the popular fully convolutional network (FCN) method. In terms of efficiency, the proposed method took around 10 hours for training with 50,000 iterations, and approximately 30 seconds for testing of a typical MRI image in the BRATS 2013 data set with a size of 160×216×176, using a DELL PRECISION workstation T7400, with an NVIDIA Tesla K20c GPU. Conclusions An effective brain tumor segmentation method for MRI images based on a HNN has been developed. The high level of accuracy and efficiency make this method practical in brain tumor segmentation. It may play a crucial role in both brain tumor diagnostic analysis and in the treatment planning of radiation therapy.

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Joint Segmentation of Image Ensembles via Latent Atlases

Cited by 5 publications

References 15 publications

Segmentation of image ensembles via latent atlases

Segmentation of image ensembles via latent atlases

Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications

Brain tumor segmentation using holistically nested neural networks in MRI images

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