Segmentation of abdominal organs in computed tomography using a generalized statistical shape model

Krasoń, Agata; Woloshuk, Andre; Spińczyk, Dominik

doi:10.1016/j.compmedimag.2019.101672

Cited by 7 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, although these other structures do generate grooves at their own boundaries, there is little practical effect to the edge detection of the target structure, because the external load is always applied to the positions near the crack tip, and therefore no large stresses will be generated in these neighboring grooves. Image (c) is a CT image of a spleen 30 . There is also interference from the boundaries of other structures, especially at the right boundary of the spleen.…”

Section: Resultsmentioning

confidence: 99%

“…There is a white area at the bottom of the left lung that is distinctly different from other tissues, and it is difficult to correctly segment this area when using an algorithm 29 ), computed tomography (CT) image of spleen (reproduced with permission from ref. 30 ), X-ray image of blood vessel (reproduced with permission from ref. 28 ), ultrasound image of blood vessel caliber, CT image of the kidney (reproduced with permission from ref.…”

Section: Resultsmentioning

confidence: 99%

“… 29 ), computed tomography (CT) image of spleen (reproduced with permission from ref. 30 ), X-ray image of blood vessel (reproduced with permission from ref. 28 ), ultrasound image of blood vessel caliber, CT image of the kidney (reproduced with permission from ref.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

Huang

et al. 2020

Nat Commun

View full text Add to dashboard Cite

Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging. Here we present a method, called the crack propagation method (CPM), based on the principles of fracture mechanics. This unique method converts the image segmentation problem into a mechanical one, extracting the boundary information of the target area by tracing the crack propagation on a thin plate with grooves corresponding to the area edge. The greatest advantage of CPM is in segmenting images involving blurred or even discontinuous boundaries, a task difficult to achieve by existing auto-segmentation methods. The segmentation results for synthesized images and real medical images show that CPM has high accuracy in segmenting complex boundaries. With increasing demand for medical imaging in clinical practice and research, this method will show its unique potential.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

Huang

et al. 2020

Nat Commun

View full text Add to dashboard Cite

show abstract

“…Another key application of organ and lesion contouring is treatment volume calculation for radiotherapy planning. However, boundary delimitation of anatomical structures in medical images remains a challenge due to their complexity, particularly in the upper abdominal cavity, where there are constant changes in the position of the different organs with the respiratory cycle, as well as the occurrence of anatomical variants and pathological changes of organs[ 30 ].…”

Section: Image Analysismentioning

confidence: 99%

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Berbís¹,

Godino²,

Val³

et al. 2023

World J Gastroenterol

View full text Add to dashboard Cite

Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians’ workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.

show abstract

“…In oncologic imaging, the accurate and automated segmentation of abdominal organs is a critical first step for the detection and delineation of tumors and metastases, and for surgical preplanning. Recent works showed robust results in the spleen, kidneys and liver segmentation on CT and MRI images with high dice scores ranging from 0.88 to 0.96 [ 16 , 17 , 18 , 19 , 20 ]. Other works demonstrated the value of imaging biomarkers in differentiating malignant lymphoma from other cancer entities [ 21 , 22 ], and even in predicting early relapse, as it has been shown by Lisson et al for mantle cell lymphoma [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly

Meddeb

Kossen

Bressem

et al. 2022

Cancers

View full text Add to dashboard Cite

Splenomegaly is a common cross-sectional imaging finding with a variety of differential diagnoses. This study aimed to evaluate whether a deep learning model could automatically segment the spleen and identify the cause of splenomegaly in patients with cirrhotic portal hypertension versus patients with lymphoma disease. This retrospective study included 149 patients with splenomegaly on computed tomography (CT) images (77 patients with cirrhotic portal hypertension, 72 patients with lymphoma) who underwent a CT scan between October 2020 and July 2021. The dataset was divided into a training (n = 99), a validation (n = 25) and a test cohort (n = 25). In the first stage, the spleen was automatically segmented using a modified U-Net architecture. In the second stage, the CT images were classified into two groups using a 3D DenseNet to discriminate between the causes of splenomegaly, first using the whole abdominal CT, and second using only the spleen segmentation mask. The classification performances were evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Occlusion sensitivity maps were applied to the whole abdominal CT images, to illustrate which regions were important for the prediction. When trained on the whole abdominal CT volume, the DenseNet was able to differentiate between the lymphoma and liver cirrhosis in the test cohort with an AUC of 0.88 and an ACC of 0.88. When the model was trained on the spleen segmentation mask, the performance decreased (AUC = 0.81, ACC = 0.76). Our model was able to accurately segment splenomegaly and recognize the underlying cause. Training on whole abdomen scans outperformed training using the segmentation mask. Nonetheless, considering the performance, a broader and more general application to differentiate other causes for splenomegaly is also conceivable.

show abstract

Segmentation of abdominal organs in computed tomography using a generalized statistical shape model

Cited by 7 publications

References 5 publications

Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly

Contact Info

Product

Resources

About