Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm

Song, Hong; Kang, Wei; Zhang, Qian; Wang, Shuliang

doi:10.1186/1752-0509-9-s5-s5

Cited by 24 publications

(11 citation statements)

References 17 publications

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“…All pixels from the image were subtracted from the average of the first and last regions of the intervertebral disk of the same subject. The disk-normalized bone marrows were segmented using a 3-dimensional GrowCut algorithm, which is a semi-automatic way of segmenting the area of interest from multiple slices of an image [11]. Figure 1 illustrates the sagittal T1-weighted images and the processed images of the controls and diseased patients, respectively.…”

Section: B Image Acquisition and Segmentationmentioning

confidence: 99%

Bone Marrow Radiomics of T1-Weighted Lumber Spinal MRI to Identify Diffuse Hematologic Marrow Diseases: Comparison With Human Readings

2020

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We developed a radiomics model to differentiate hematologic marrow diseases and compared the performance with radiologists' readings and a quantitative measurement. Patients were retrospectively analyzed from the diseased (n = 254) and control groups (n = 230). A sagittal T1-weighted lumbar spinal MR image was normalized by an intervertebral disk, and bone marrow was segmented. A hundred features were extracted, and final features were selected using Principle Component Analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Finally, Random forest (RF) and logistic regression (LR) models were trained. Two radiologists with different levels of experience analyzed the images for the presence of bone marrow diseases, independently. The area under the receiver operating characteristic curves (AUC) and decision curve analysis (DCA) was evaluated. Among the subjects, 363 cases were assigned as a training set and 121 as a validation set. The combination of LASSO and RF produced the best results. With the validation set, the sensitivity (SE) was 87.3%, specificity (SP) was 86.2% and AUC was 0.928 (p < 0.05). We selected Firstorder-Maximum as the best feature to identify diseased marrows, which achieved SE of 75.0% and AUC of 0.787 (p < 0.05). The reader with 11 years of experience yielded SE of 86.5% and AUC of 0.861 (p < 0.05). The second reader with 1 year of experience yielded SE of 75.0% and AUC of 0.767 (p < 0.05). We demonstrated the advantage of bone marrow radiomics over conventional methods of diagnosing with radiologists' readings and quantitative measurements.

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Section: B Image Acquisition and Segmentationmentioning

confidence: 99%

Bone Marrow Radiomics of T1-Weighted Lumber Spinal MRI to Identify Diffuse Hematologic Marrow Diseases: Comparison With Human Readings

2020

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“…Song et. al proposed the based Fuzzy C-means algorithm with spatial information algorithm and GrowCut algorithm segmentation process [15]. They reported a sensitivity of 95.46% with specificity of 99.82%.…”

Section: Related Workmentioning

confidence: 99%

Spinal Cord Based Kidney Segmentation Using Connected Component Labeling and K-Means Clustering Algorithm

Tuncer¹,

Alkan²

2019

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Abdominal computed tomography (CT) data are often used in the diagnosis and treatment of patients. Segmentation of viscera on abdominal imaging facilitates diagnosis and focus upon the areas of interest. Kidney segmentation by abdominal imaging is complicated by the proximity of various organs and the similarities between abdominal tissues. Here we propose two fully automated approaches to kidney segmentation and discuss their performance. A fully automated approach was preferred to accelerate the decision-making process of the physician to eliminate the disadvantage of manual and semi-automatic segmentations. Each of the proposed methods essentially consist of three stages. Since the spine was used as reference in the study, the images were first treated to define the coordinates of the spine. Second, kidney fields were obtained using the Connected Component Labeling (CCL) and the K-means clustering algorithms. Last, the kidneys were segmented by applying different filters according to the method. A manual segmentation was then performed by specialist physicians. The performance of the tested algorithms was made by comparison to the manual segmentation results, using the Dice Similarity Coefficient, the Figures of Merit and Jaccard Similarity Index. Based on our analyses, acceptable success rates were achieved by the proposed methodologies. These automated systems are expected to be helpful during clinical diagnosis, medical training and future studies on kidney cancer diagnosis.

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“…The parameters includes of length, lateral diameter, anteriorposterior diameter [4]. There is also a study that proposed an improved method of Fuzzy C Means and Graph Cut method in kidney segmentation [5]. A semi-automated method for kidney segmentation has been proposed by Jun Xie et.al which is by using both texture and shape segmentation method [6].…”

Section: Introductionmentioning

confidence: 99%

“…Different methods of kidneys segmentation have been developed over the recent years including Fuzzy CMeans Clustering [3,5], Graph Cut [5], Edge Based [2] and Region Growing Method [2]. Moe Moe and Theingi proposed an Edge Based method is more suitable compared to Region Growing Method in kidney segmentation [2].…”

Section: Introductionmentioning

confidence: 99%