A method for extracting multi-organ from four-phase contrasted CT images based on CT value distribution estimation using EM-algorithm

Makiko, Sakashita; Kitasaka, Takayuki; Mori, Kensaku; Suenaga, Yasuhito; Nakamura, Shigeru

doi:10.1117/12.709232

Cited by 11 publications

(10 citation statements)

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“…Seifert et al 71 obtained 2.1Ϯ 0.5 ASD, Zhang et al 65 reported a coincidence ratio of 96% and an average error rate of 4.3%, while Sakashita et al 70 segmented the spleen with a recall ratio of 73.2%. Table III presents the validation metrics using our technique to segment the spleen.…”

Section: Discussionmentioning

confidence: 99%

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Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation

et al. 2010

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Purpose:To investigate the potential of the normalized probabilistic atlases and computer-aided medical image analysis to automatically segment and quantify livers and spleens for extracting imaging biomarkers ͑volume and height͒. Methods: A clinical tool was developed to segment livers and spleen from 257 abdominal contrastenhanced CT studies. There were 51 normal livers, 44 normal spleens, 128 splenomegaly, 59 hepatomegaly, and 23 partial hepatectomy cases. 20 more contrast-enhanced CT scans from a public site with manual segmentations of mainly pathological livers were used to test the method. Data were acquired on a variety of scanners from different manufacturers and at varying resolution. Probabilistic atlases of livers and spleens were created using manually segmented data from ten noncontrast CT scans ͑five male and five female͒. The organ locations were modeled in the physical space and normalized to the position of an anatomical landmark, the xiphoid. The construction and exploitation of liver and spleen atlases enabled the automated quantifications of liver/spleen volumes and heights ͑midhepatic liver height and cephalocaudal spleen height͒ from abdominal CT data. The quantification was improved incrementally by a geodesic active contour, patient specific contrast-enhancement characteristics passed to an adaptive convolution, and correction for shape and location errors. Results: The livers and spleens were robustly segmented from normal and pathological cases. For the liver, the Dice/Tanimoto volume overlaps were 96.2%/92.7%, the volume/height errors were 2.2%/2.8%, the root-mean-squared error ͑RMSE͒ was 2.3 mm, and the average surface distance ͑ASD͒ was 1.2 mm. The spleen quantification led to 95.2%/91% Dice/Tanimoto overlaps, 3.3%/ 1.7% volume/height errors, 1.1 mm RMSE, and 0.7 ASD. The correlations ͑R 2 ͒ with clinical/ manual height measurements were 0.97 and 0.93 for the spleen and liver, respectively ͑p Ͻ 0.0001͒. No significant difference ͑p Ͼ 0.2͒ was found comparing interobserver and automaticmanual volume/height errors for liver and spleen. Conclusions: The algorithm is robust to segmenting normal and enlarged spleens and livers, and in the presence of tumors and large morphological changes due to partial hepatectomy. Imaging biomarkers of the liver and spleen from automated computer-assisted tools have the potential to assist the diagnosis of abdominal disorders from routine analysis of clinical data and guide clinical management.

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Section: Discussionmentioning

confidence: 99%

“…69 and 70. Hu et al 69 used independent component analysis in a variational Bayesian mixture, while Sakashita et al 70 combined expectation-maximization and principal component analysis to segment abdominal CT. Very recently, Seifert et al 71 proposed a semantic navigation for fast multiorgan segmentation from CT data.…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation

et al. 2010

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“…Both methods use measures of relationship and hierarchy between organs and manual landmarks. Finally, multi-dimensional CT data from fourphases are employed in [3,7]. Hu et al [3] use independent component analysis in a variational Bayesian mixure, while Sakashita et al [7] combine expectation-maximization and principal component analysis to segment abdominal CT.…”

Section: Introductionmentioning

confidence: 99%

Multi-organ automatic segmentation in 4D contrast-enhanced abdominal CT

Linguraru¹,

Summers²

2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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Medical imaging and computer-aided diagnosis (CAD) traditionally focus on organ-or disease-based applications.To shift from organ-based to organism-based approaches, CAD needs to replicate the work of radiologists and analyze consecutively multiple organs. A fully automatic method is presented for the simultaneous segmentation of four abdominal organs from 4D CT data. Abdominal contrastenhanced CT scans from sixteen patients were obtained at three phases: non-contrast, arterial and portal. Intrapatient data is registered non-rigidly using the demons algorithm and smoothed with anisotropic diffusion. Mutual information accounts for intensity variability within the same organ during subsequent acquisitions and data are interpolated with cubic B-splines. Then heterogeneous erosion is applied to multi-phase data using the intensity characteristics of the liver, spleen, and kidneys. The erosion filter is a 4D convolution that preserves only image regions that satisfy the above intensity criteria. Finally, a geodesic level set completes the segmentation of the four abdominal organs. This 3D evaluation of abdominal data shows great promise as a computer-aided radiology tool for multi-organ and multi-disease analysis.

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“…Batın bölgesi organlarının birlikte bölütlenmesi konusunda BT alanında başarılı çalışmalar olmasına karşın [1][2][3][4][5][6][7][8], görece daha yeni olan ve özellikleri sürekli gelişen Manyetik Rezonans (MR) görüntülerinde batın bölgesi organlarının bölütlenmesinde, organ bazında yapılan çalışmalar ağırlıklıdır. Bu çalışmalar özellikle karaciğer üzerine yoğunlaşmış olup [9][10][11][12], böbrekler [13], dalak [14], yağ ve kas dokularının [15] bölütlenmesi ile ilgili çalışmalar bulunmaktadır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Batin Bölgesi̇ Organlarinin MR Görüntüleri̇nden Çok Aşamali Hi̇yerarşi̇k Siniflama İle Bölütlenmesi̇

Selvi¹,

Selver²,

Kavur³

et al. 2015

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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ÖZETTıbbi görüntüleme ile anatomi hakkında ayrıntılı bilgi elde edinilebildiğinden, tanı amaçlı görüntüleme günümüzde birçok açıdan önem kazanmıştır. Görüntüleme cihazları tarafından sunulan verilerin fazlalığı ve çeşitliliği nedeniyle, tüm veri yerine görüntülerde sadece ilgilenilen dokunun belirlenerek ayrılması (Bölütlenmesi) sağlanabilir. Elcil yöntemler ile bölütleme yorucu, zaman alıcı ve deneyim gerektiren bir işlem olduğundan, otomatik yordamlara gereksinim duyulmaktadır. Geliştirilen yordamların klinik koşullarında kullanılabilmesi içinse yüksek başarıma sahip sonuçlar üretmeleri gerekmektedir. Manyetik Rezonans (MR) görüntülerinden batın bölgesindeki organlarının bölütlenmesi pek çok zorluk içeren bir uygulama alanıdır ve bu konudaki çalışmalar sınırlı sayıdadır. Batın bölgesinde yer alan, karaciğer, böbrekler, dalak, pankreas, safra kesesi gibi organların MR görüntüleri kullanılarak ileri seviye tıbbi analizi ve üç boyutlu incelenmesi pek çok tıbbi prosedür için mecburi olduğundan, bu çalışmada, ilgili organların bölütlenmesinde yukarıda belirtilen zorluklara karşı gürbüz, bölütlenecek organın özellikleri ve organların birbirleriyle olan ilişkilerini (konum vb.) göz önüne alan bir sistem geliştirilmiştir. Geliştirilen sistem farklı MR sekansları ile elde edilen görüntülere uygulanarak elde edilen sonuçlar tartışılmıştır.Anahtar Kelimeler: Bölütleme, MR, hiyerarşik sınıflama, batın SEGMENTATION OF ABDOMINAL ORGANS FROM MR IMAGES USING MULTI-LEVEL HIERARCHICAL CLASSIFICATION ABSTRACTMedical imaging modalities can provide very detailed and informative mappings of the anatomy of a subject. These detailed and informative mappings can be processed to extract the information of interest instead of dealing with whole data (segmentation). Since manual segmentation on each slice is time consuming, tedious and operator dependent, automatic tools and techniques are needed. Segmentation of abdominal organs is a very challenging field of application due to overlapping intensity ranges of the organs, variations in human anatomy and pathology and the number of studies is very limited for Magnetic Resonance (MR), which is a relatively newer and rapidly developing imaging modality.Since it is obligatory to analyze and visualize MR images of abdominal organs (i.e. liver, right/left kidneys, spleen, pancreas, gall bladder) for several medical procedures, the main goal of this paper is to design and develop a segmentation system (method+software), which is robust to the challenges mentioned above, adaptive to the properties of the abdominal organs as well as to the interrelationships of these organs.

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A method for extracting multi-organ from four-phase contrasted CT images based on CT value distribution estimation using EM-algorithm

Cited by 11 publications

References 0 publications

Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation

Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation

Multi-organ automatic segmentation in 4D contrast-enhanced abdominal CT

Batin Bölgesi̇ Organlarinin MR Görüntüleri̇nden Çok Aşamali Hi̇yerarşi̇k Siniflama İle Bölütlenmesi̇

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