scholarly journals AN AUTOMATIC SEGMENTATION OF LIVER COLUME THAT CONTAINED DISCONNECTED REGION WITH DYNAMIC LOCAL NEIGHBOURHOOD REGION SIZES

2016 ◽  
Vol 3 (1) ◽  
pp. 129-148
Author(s):  
Puteri Suhaiza Sulaiman ◽  
Rahmita Wirza Rahmat ◽  
Ramlan Mahmod ◽  
Abdul Hamid Abdul Rashid
Keyword(s):  
Level Set ◽  
Automatic Segmentation ◽  
Dice Similarity Coefficient ◽  
Dynamic Localization ◽  
Hybrid Energy ◽  
Segmentation Accuracy ◽  
Average Factor ◽  
Liver Region ◽  
Level Set Framework ◽  
Localization Region

Segmentation of liver images containing disconnected regions has always been an overlooked problem. Previous works on liver segmentation either ignore this problem or use manual initialization when facing these disconnected regions. Therefore, in this paper we propose a liver level set (LLS) algorithm which is able to segment disconnected regions automatically. The LLS algorithm is based on level set framework together with hybrid energy minimization as the stopping function. By using the LLS algorithm in a looping manner, we allow the current liver boundary to inherit the topological changes from previous images in a 2.5D environment. We also conduct an experiment to obtain an average factor for dynamic localization region sizes based on liver anatomy to improve the segmentation accuracy. These dynamic localization region sizes ensure a more accurate segmentation when compared with manual segmentation. Our experiment gives a respective segmentation result with dice similarity coefficient (DSC) percentage of 87.5%. Plus, our LLS algorithm is able to segment all connected and disconnected liver region automatically and accurately.

scholarly journals Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines

2018 ◽  
Vol 8 (9) ◽  
pp. 1586 ◽  
Author(s):  
Sewon Kim ◽  
Won Bae ◽  
Koichi Masuda ◽  
Christine Chung ◽  
Dosik Hwang
Keyword(s):  
Lumbar Spine ◽  
Vertebral Body ◽  
Automatic Segmentation ◽  
Dice Similarity Coefficient ◽  
Mr Images ◽  
Segmentation Accuracy ◽  
Body Segmentation ◽  
Spine Mr ◽  
Vertebral Bodies ◽  
Automatic Algorithm

We propose a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance (MR) images of the human lumbar spine. Quantitative analysis of spine MR images often necessitate segmentation of the image into specific regions representing anatomic structures of interest. Existing algorithms for vertebral body segmentation require heavy inputs from the user, which is a disadvantage. For example, the user needs to define individual regions of interest (ROIs) for each vertebral body, and specify parameters for the segmentation algorithm. To overcome these drawbacks, we developed a semi-automatic algorithm that considerably reduces the need for user inputs. First, we simplified the ROI placement procedure by reducing the requirement to only one ROI, which includes a vertebral body; subsequently, a correlation algorithm is used to identify the remaining vertebral bodies and to automatically detect the ROIs. Second, the detected ROIs are adjusted to facilitate the subsequent segmentation process. Third, the segmentation is performed via graph-based and line-based segmentation algorithms. We tested our algorithm on sagittal MR images of the lumbar spine and achieved a 90% dice similarity coefficient, when compared with manual segmentation. Our new semi-automatic method significantly reduces the user’s role while achieving good segmentation accuracy.

scholarly journals Multi-scale U-like network with attention mechanism for automatic pancreas segmentation

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252287
Author(s):  
Yingjing Yan ◽  
Defu Zhang
Keyword(s):  
Spatial Information ◽  
Rapid Development ◽  
Automatic Segmentation ◽  
Attention Mechanism ◽  
Dice Similarity Coefficient ◽  
Segmentation Accuracy ◽  
Third Dimension ◽  
Computational Resources ◽  
High Degree ◽  
Segmentation Models

In recent years, the rapid development of deep neural networks has made great progress in automatic organ segmentation from abdominal CT scans. However, automatic segmentation for small organs (e.g., the pancreas) is still a challenging task. As an inconspicuous and small organ in the abdomen, the pancreas has a high degree of anatomical variability and is indistinguishable from the surrounding organs and tissues, which usually leads to a very vague boundary. Therefore, the accuracy of pancreatic segmentation is sometimes below satisfaction. In this paper, we propose a 2.5D U-net with an attention mechanism. The proposed network includes 2D convolutional layers and 3D convolutional layers, which means that it requires less computational resources than 3D segmentation models while it can capture more spatial information along the third dimension than 2D segmentation models. Then We use a cascaded framework to increase the accuracy of segmentation results. We evaluate our network on the NIH pancreas dataset and measure the segmentation accuracy by the Dice similarity coefficient (DSC). Experimental results demonstrate a better performance compared with state-of-the-art methods.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

2021 ◽  
Author(s):  
Jorge F. Lazo ◽  
Aldo Marzullo ◽  
Sara Moccia ◽  
Michele Catellani ◽  
Benoit Rosa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Temporal Information ◽  
Invasive Technique ◽  
Dice Similarity Coefficient ◽  
Specular Reflections ◽  
Lumen Segmentation ◽  
Previous State

Abstract Purpose Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma. During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on convolutional neural networks (CNNs). Methods The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks ($$m_1$$ m 1 ) and Mask-RCNN ($$m_2$$ m 2 ), which are fed with single still-frames I(t). The other two models ($$M_1$$ M 1 , $$M_2$$ M 2 ) are modifications of the former ones consisting on the addition of a stage which makes use of 3D convolutions to process temporal information. $$M_1$$ M 1 , $$M_2$$ M 2 are fed with triplets of frames ($$I(t-1)$$ I ( t - 1 ) , I(t), $$I(t+1)$$ I ( t + 1 ) ) to produce the segmentation for I(t). Results The proposed method was evaluated using a custom dataset of 11 videos (2673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in the presence of poor visibility, occasional bleeding, or specular reflections.

scholarly journals Quantitative salivary gland SPECT/CT using deep convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Junyoung Park ◽  
Jae Sung Lee ◽  
Dongkyu Oh ◽  
Hyun Gee Ryoo ◽  
Jeong Hee Han ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Salivary Gland ◽  
Single Photon ◽  
Automatic Segmentation ◽  
Photon Emission ◽  
Operating Time ◽  
Emission Computed Tomography ◽  
Dice Similarity Coefficient ◽  
Deep Convolutional Neural Networks ◽  
Gland Function

AbstractQuantitative single-photon emission computed tomography/computed tomography (SPECT/CT) using Tc-99m pertechnetate aids in evaluating salivary gland function. However, gland segmentation and quantitation of gland uptake is challenging. We develop a salivary gland SPECT/CT with automated segmentation using a deep convolutional neural network (CNN). The protocol comprises SPECT/CT at 20 min, sialagogue stimulation, and SPECT at 40 min post-injection of Tc-99m pertechnetate (555 MBq). The 40-min SPECT was reconstructed using the 20-min CT after misregistration correction. Manual salivary gland segmentation for %injected dose (%ID) by human experts proved highly reproducible, but took 15 min per scan. An automatic salivary segmentation method was developed using a modified 3D U-Net for end-to-end learning from the human experts (n = 333). The automatic segmentation performed comparably with human experts in voxel-wise comparison (mean Dice similarity coefficient of 0.81 for parotid and 0.79 for submandibular, respectively) and gland %ID correlation (R2 = 0.93 parotid, R2 = 0.95 submandibular) with an operating time less than 1 min. The algorithm generated results that were comparable to the reference data. In conclusion, with the aid of a CNN, we developed a quantitative salivary gland SPECT/CT protocol feasible for clinical applications. The method saves analysis time and manual effort while reducing patients’ radiation exposure.

A versatile algorithm for the automatic segmentation of hippocampus based on level set

2011 ◽  
Vol 7 (3) ◽  
pp. 213 ◽  
Author(s):  
J. Rajeesh ◽  
R.S. Moni ◽  
S. Palanikumar ◽  
T. Gopalakrishnan
Keyword(s):  
Level Set ◽  
Automatic Segmentation

scholarly journals Automatic speech segmentation using throat-acoustic correlation coefficients

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Rustam Rafikovich Mussabayev ◽  
Maksat N. Kalimoldayev ◽  
Yedilkhan N. Amirgaliyev ◽  
Timur R. Mussabayev
Keyword(s):  
Large Volume ◽  
Automatic Segmentation ◽  
Correlation Coefficients ◽  
High Accuracy ◽  
Speech Segmentation ◽  
Preliminary Training ◽  
Effi Ciency ◽  
Segmentation Accuracy ◽  
New Type ◽  
The Given

Abstract This work considers one of the approaches to the solution of the task of discrete speech signal automatic segmentation. The aim of this work is to construct such an algorithm which should meet the following requirements: segmentation of a signal into acoustically homogeneous segments, high accuracy and segmentation speed, unambiguity and reproducibility of segmentation results, lack of necessity of preliminary training with the use of a special set consisting of manually segmented signals. Development of the algorithm which corresponds to the given requirements was conditioned by the necessity of formation of automatically segmented speech databases that have a large volume. One of the new approaches to the solution of this task is viewed in this article. For this purpose we use the new type of informative features named TAC-coefficients (Throat-Acoustic Correlation coefficients) which provide sufficient segmentation accuracy and effi- ciency.

scholarly journals Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Hideaki Hirashima ◽  
Mitsuhiro Nakamura ◽  
Pascal Baillehache ◽  
Yusuke Fujimoto ◽  
Shota Nakagawa ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Volumetric Modulated Arc Therapy ◽  
Intensity Modulated Radiotherapy ◽  
Deep Convolutional Neural Network ◽  
Training Dataset ◽  
Dice Similarity Coefficient ◽  
Optimal Model ◽  
Pelvic Region ◽  
Segmentation Accuracy

Abstract Background This study aimed to (1) develop a fully residual deep convolutional neural network (CNN)-based segmentation software for computed tomography image segmentation of the male pelvic region and (2) demonstrate its efficiency in the male pelvic region. Methods A total of 470 prostate cancer patients who had undergone intensity-modulated radiotherapy or volumetric-modulated arc therapy were enrolled. Our model was based on FusionNet, a fully residual deep CNN developed to semantically segment biological images. To develop the CNN-based segmentation software, 450 patients were randomly selected and separated into the training, validation and testing groups (270, 90, and 90 patients, respectively). In Experiment 1, to determine the optimal model, we first assessed the segmentation accuracy according to the size of the training dataset (90, 180, and 270 patients). In Experiment 2, the effect of varying the number of training labels on segmentation accuracy was evaluated. After determining the optimal model, in Experiment 3, the developed software was used on the remaining 20 datasets to assess the segmentation accuracy. The volumetric dice similarity coefficient (DSC) and the 95th-percentile Hausdorff distance (95%HD) were calculated to evaluate the segmentation accuracy for each organ in Experiment 3. Results In Experiment 1, the median DSC for the prostate were 0.61 for dataset 1 (90 patients), 0.86 for dataset 2 (180 patients), and 0.86 for dataset 3 (270 patients), respectively. The median DSCs for all the organs increased significantly when the number of training cases increased from 90 to 180 but did not improve upon further increase from 180 to 270. The number of labels applied during training had a little effect on the DSCs in Experiment 2. The optimal model was built by 270 patients and four organs. In Experiment 3, the median of the DSC and the 95%HD values were 0.82 and 3.23 mm for prostate; 0.71 and 3.82 mm for seminal vesicles; 0.89 and 2.65 mm for the rectum; 0.95 and 4.18 mm for the bladder, respectively. Conclusions We have developed a CNN-based segmentation software for the male pelvic region and demonstrated that the CNN-based segmentation software is efficient for the male pelvic region.

scholarly journals A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning

2021 ◽  
Author(s):  
Wing Keung Cheung ◽  
Robert Bell ◽  
Arjun Nair ◽  
Leon Menezies ◽  
Riyaz Patel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Arteries ◽  
Automatic Segmentation ◽  
Three Dimensional ◽  
Regions Of Interest ◽  
Dice Similarity Coefficient ◽  
Processing Unit ◽  
Two Dimensional ◽  
Computed Tomography Images ◽  
Graphical Processing

AbstractA fully automatic two-dimensional Unet model is proposed to segment aorta and coronary arteries in computed tomography images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively. Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published two-dimensional or three-dimensional deep learning models. Furthermore, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed within hospital computer networks where graphical processing units are typically not available.

Medical Imaging Importance in the Real World

2020 ◽  
pp. 136-162
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Level Set ◽  
Real World ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Medical Image Segmentation ◽  
Image Resolution ◽  
Variational Model ◽  
Medical Applications

In the medical image resolution, automatic segmentation is a challenging task, and it's still an unsolved problem for most medical applications due to the wide variety connected with image modalities, encoding parameters, and organic variability. In this chapter, a review and critique of medical image segmentation using clustering, compression, histogram, edge detection, parametric, variational model. and level set-based methods is presented. Modes of segmentation like manual, semi-automatic, interactive, and automatic are also discussed. To present current challenges, aim and motivation for doing fast, interactive and correct segmentation, the medical image modalities X-ray, CT, MRI, and PET are discussed in this chapter.

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