3D Segmentation for Multi-Organs in CT Images

Bajger, Mariusz; Caon, Martin

doi:10.5565/rev/elcvia.516

Cited by 13 publications

(3 citation statements)

References 30 publications

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“…When compared to previous multi-organ segmentation studies, the results of this study are comparable or better. A multi-organ segmentation study using statistical region merging method reported DSC values of 0.95 for lungs, 0.93 for heart and kidneys, an average of 0.90 for liver and spleen, and an average of 0.86 for spinal cord and bones for a single adult test patient (Bajger et al 2013). A study using entangled decision trees reported an overall Jaccard overlapping index of 56% for eight organs from 50 patient scans from various CT vendors (Montillo et al 2011).…”

Section: Discussionmentioning

confidence: 99%

Fully automated tissue classifier for contrast-enhanced CT scans of adult and pediatric patients

et al. 2018

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Purpose: To develop a consistent, fully-automated classifier for all tissues within the trunk and to more accurately discriminate between tissues (such as bone) and contrast medium with overlapping high CT numbers Methods: Twenty-eight contrast enhanced NCAP (neck-chest-abdomen-pelvis) CT scans (4 adult and 3 pediatric patients) were used to train and test a tissue classification pipeline. The classifier output consisted of 6 tissue classes: lung, fat, muscle, solid organ, blood/bowel contrast and bone. The input features for training were selected from 28 2D image filters and 12 3D filters, and one hand crafted spatial feature. To improve differentiation between tissue and blood/bowel contrast classification, 70 additional CT images were manually classified. Two different training data sets consisting of manually classified tissues from different locations in body were used to train the models. Training used the random forest algorithm in WEKA (Waikato Environment for Knowledge Analysis); the number of trees was optimized for best out-of-bag error. Automated classification accuracy was compared with manual classification by calculating Dice Similarity Coefficient (DSC). Results: Model performance was tested on 21 manually classified slices (2 adult and 1 pediatric patient). The overall DSC at image locations represented in the training dataset were - lung: 0.98, fat: 0.90, muscle: 0.85, solid organ: 0.75, blood/contrast: 0.82, and bone: 0.90. The overall DSC for slice locations that were not represented in the training dataset were - lung: 0.97, fat: 0.89, muscle: 0.76, solid organ: 0.79, blood: 0.56, and bone: 0.74. Analyzing the classification maps for the entire scan volume revealed that except for misclassifications in the trabecular bone region of the spinal column, and solid organ and blood/contrast interfaces within the abdomen, the results were acceptable. Conclusions: A fully-automated whole-body tissue classifier for adult and pediatric contrast-enhanced CT using random forest algorithm and intensity-based image filters was developed.

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Section: Discussionmentioning

confidence: 99%

Fully automated tissue classifier for contrast-enhanced CT scans of adult and pediatric patients

et al. 2018

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“…The segmentation of multiple organs using standard graph-based approaches has been successfully explored for the abdominal and thoracic region (Bajger et al, 2013;Dong et al, 2016;Linguraru and Summers, 2014). However, the bottom-up approach used in these models presents an important limitation: the need for additional constraints to ensure the anatomical consistency of the results in a complex multi-organ scenario.…”

Section: Graph-based Modelsmentioning

confidence: 99%

“…GlM► Smyth et al, 1997; MuLeM► Bernard et al, 2001;Lecron et al, 2012a;Lecron et al, 2012b;Neubert et al, 2014;SeqM► de Bruijne et al 2007; ArtM► Ali et al, 2012;Bois et al, 2005;Boisvert et al, 2006;Boisvert et al, 2008a;Boisvert et al, 2008b;Desroches et al, 2007;Gill et al, 2012;Harmouche et al, 2012;Kadoury and Paragios, 2009;Klinder et al, 2008;Monheit and Badler, 1991;Moura et al, 2011;Panjabi et al, 1976;Rasoulian et al, 2013 SeqM► Camara et al, 2004;He et al, 2015;Kéchichian et al, 2014;Matsumoto and Udupa, 2013;Sun et al, 2016;Udupa et al, 2011;Udupa et al, 2013;Wang et al, 2014a;Wang et al, 2014b;MaLrM► Criminisi et al, 2009;Iglesias et al, 2011;Keraudren et al, 2015;Mansoor et al, 2017;Mansoor et al, 2018;Montillo et al, 2011;Pauly et al, 2011;de Vos et al, 2017; AtM► Gubern-Mérida et al, 2011;Schreibmann et al, 2014; GrM► Bajger et al, 2013;Kéchichian et al, 2014; Heart GlM►…”

Section: Head and Neckunclassified

Computational anatomy for multi-organ analysis in medical imaging: A review

Cerrolaza

Picazo

Humbert³

et al. 2019

Medical Image Analysis

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The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of medical imaging applications on the future of healthcare.

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Paediatric Liver Segmentation for Low-Contrast CT Images

Bajger

Caon

2018

Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis

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3D Segmentation for Multi-Organs in CT Images

Cited by 13 publications

References 30 publications

Fully automated tissue classifier for contrast-enhanced CT scans of adult and pediatric patients

Fully automated tissue classifier for contrast-enhanced CT scans of adult and pediatric patients

Computational anatomy for multi-organ analysis in medical imaging: A review

Paediatric Liver Segmentation for Low-Contrast CT Images

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