Imaging of Spleen Disorders

Paley, Mark R.; Ros, Pablo R.

doi:10.1007/978-1-59259-124-4_16

Cited by 7 publications

(5 citation statements)

References 102 publications

(27 reference statements)

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“…Accurate non-invasive spleen volumetric size estimation plays an essential role in splenomegaly diagnosis and scientific studies [2]. Ultrasound [3–5] and computerized tomography (CT) [6–8] have been used as the major imaging techniques in quantifying spleen size [9, 10].…”

Section: Introductionmentioning

confidence: 99%

Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly

Huo

Liu

et al. 2017

SPIE Proceedings

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Non-invasive spleen volume estimation is essential in detecting splenomegaly. Magnetic resonance imaging (MRI) has been used to facilitate splenomegaly diagnosis in vivo. However, achieving accurate spleen volume estimation from MR images is challenging given the great inter-subject variance of human abdomens and wide variety of clinical images/modalities. Multi-atlas segmentation has been shown to be a promising approach to handle heterogeneous data and difficult anatomical scenarios. In this paper, we propose to use multi-atlas segmentation frameworks for MRI spleen segmentation for splenomegaly. To the best of our knowledge, this is the first work that integrates multi-atlas segmentation for splenomegaly as seen on MRI. To address the particular concerns of spleen MRI, automated and novel semi-automated atlas selection approaches are introduced. The automated approach interactively selects a subset of atlases using selective and iterative method for performance level estimation (SIMPLE) approach. To further control the outliers, semi-automated craniocaudal length based SIMPLE atlas selection (L-SIMPLE) is proposed to introduce a spatial prior in a fashion to guide the iterative atlas selection. A dataset from a clinical trial containing 55 MRI volumes (28 T1 weighted and 27 T2 weighted) was used to evaluate different methods. Both automated and semi-automated methods achieved median DSC > 0.9. The outliers were alleviated by the L-SIMPLE (≈1 min manual efforts per scan), which achieved 0.9713 Pearson correlation compared with the manual segmentation. The results demonstrated that the multi-atlas segmentation is able to achieve accurate spleen segmentation from the multi-contrast splenomegaly MRI scans.

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Section: Introductionmentioning

confidence: 99%

Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly

Huo

Liu

et al. 2017

SPIE Proceedings

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“…Spleen volume is used for both clinical and pharmacological purposes. Clinically, abnormal spleen volume has important diagnostic value in the diagnosis of liver diseases, blood cancer -specifically myeloproliferative neoplasms and lymphomas -infections and some forms of anemia [6,7]. Pharmacologically, spleen volume can be a dosage indicator for prescription or for the assessment of drug effectiveness after administration and planning of surgical procedures.…”

Section: Introductionmentioning

confidence: 99%

Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline

Moon

Huo

Abramson

et al. 2019

Computers in Biology and Medicine

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Delineation of Computed Tomography (CT) abdominal anatomical structure, specifically spleen segmentation, is useful for not only measuring tissue volume and biomarkers but also for monitoring interventions. Recently, segmentation algorithms using deep learning have been widely used to reduce time humans spend to label CT data. However, the computerized segmentation has two major difficulties: managing intermediate results (e.g., resampled scans, 2D sliced image for deep learning), and setting up the system environments and packages for autonomous execution. To overcome these issues, we propose an automated pipeline for the abdominal spleen segmentation. This pipeline provides an end-to-end synthesized process that allows users to avoid installing any packages and to deal with the intermediate results locally. The pipeline has three major stages: pre-processing of input data, segmentation of spleen using deep learning, 3D reconstruction with the generated labels by matching the segmentation results with the original image dimensions, which can then be used later and for display or demonstration.

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“…The main reason why the deep learning method (CT-DL) severely underestimated splenic volume for several patients was due to the splenic size being much larger than the reported normal range of 237 ± 77 ml [32], which is encountered in patients with HL. Such discrepancies have important clinical implications for measurement of spleen volume, for example in the assessment of drug effectiveness, surgical planning and other settings [33]. In contrast to CT-DL, which was trained on participants with normal sized organs, the multi-atlas method was developed in the patients included in this study and, therefore, was better at segmenting organs in some patients who were outliers using the CT-DL method.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [¹⁸F]FDG PET-CT images

Krokos,

Kotwal,

Malaih

et al. 2024

Biomed. Phys. Eng. Express

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Utilisation of whole-organ volumes to extract anatomical and functional information from computed tomography (CT) and positron emission tomography (PET) images may provide key information for the treatment and follow-up of cancer patients. However, manual organ segmentation, is laborious and time-consuming.In this study, a CT-based deep learning method and a multi-atlas method were evaluated for segmenting the liver and spleen on CT images to extract quantitative tracer information from Fluorine-18 fluorodeoxyglucose ([18F]FDG) PET images of 50 patients with advanced Hodgkin lymphoma (HL). Manual segmentation was used as the reference method. The two automatic methods were also compared with a manually defined volume of interest (VOI) within the organ, a technique commonly performed in clinical settings.Both automatic methods provided accurate CT segmentations, with the deep learning method outperforming the multi-atlas with a DICE coefficient of 0.93 ± 0.03 (mean ± standard deviation) in liver and 0.87 ± 0.17 in spleen compared to 0.87 ± 0.05 (liver) and 0.78 ± 0.11 (spleen) for the multi-atlas. Similarly, a mean relative error of -3.2% for the liver and -3.4% for the spleen across patients was found for the mean standardized uptake value (SUVmean) using the deep learning regions while the corresponding errors for the multi-atlas method were -4.7% and -9.2%, respectively. For the maximum SUV (SUVmax), both methods resulted in higher than 20% overestimation due to the extension of organ boundaries to include neighbouring, high-uptake regions. The conservative VOI method which did not extend into neighbouring tissues, provided a more accurate SUVmax estimate.In conclusion, the automatic, and particularly the deep learning method could be used to rapidly extract information of the SUVmean within the liver and spleen. However, activity from neighbouring organs and lesions can lead to high biases in SUVmax and a manually defined volume of interest should be considered instead.

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Imaging of Spleen Disorders

Cited by 7 publications

References 102 publications

Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly

Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly

Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline

Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [¹⁸F]FDG PET-CT images

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Imaging of Spleen Disorders

Cited by 7 publications

References 102 publications

Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly

Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly

Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline

Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [18F]FDG PET-CT images

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Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [¹⁸F]FDG PET-CT images