In brain MR images , the boundary of tumor tissue is highly irregular . Deformable models and Region based methods are extensively used for medical image segmentation , to locate the boundary of the tumor . Problems associated with non-linear distribution of real data , User interaction and poor convergence to the boundary region limited their usefulness . Advanced medical imaging techniques require high performance segmentation algorithms . Extracting the structures of interest accurately is one of the main challenges in medical imaging segmentation . In this paper a methodological approach based on active contour is proposed for fully automated segmentation of radius bone . Therefore , accurate segmentation of brain images is a very difficult task . In this paper we present an automatic method of brain segmentation for detection of tumor . The MR images from T1 , T2 and flair sequences are used for the study along with axial , coronal and sagitial slices . So brain image segmentation is very important but most challenging problem among medical images . This paper depicts the different challenges and applications of brain image segmentation which can be extended in many directions of brain images . In this paper we review the different modalities of medical images like MRI , CT , and SPECT etc . Multi-atlas based label fusionmethods have been successfully used for medical image segmentation . In the field of brain region segmentation , multi-atlas based methods propagate labels from multiple atlases to target image by the similarity between patches in target image and atlases . Most of existing multi-atlas based methods usually use intensity feature , which is hard to capture high-order information in brain images . A level-set based active contour stage is performed for lesion delineation , using gradient and shape properties combined with previously learned region intensity statistics . In the proposed scheme there is no need for preregistration of an atlas , a common characteristic in brain segmentation schemes . Experimental results on real data are presented . In the image normalization step , intensity non-uniformity is removed . In the brain segmentation step , we use mathematical morphological operators and masking . The proposed algorithm was tested with twenty 3D MR normal brain image sets . These algorithms have a mean Dice Similarity Coefficient ( DSC ) of 78.35 % with maximum and minimum DSC as 89.7 % and 56.46 % respectively . The availability of better computation power and large datasets will further enhance the performance of pancreas segmentation and related tasks . The approaches presented in this paper can be easily modified to the multi organ segmentation methods by changing the loss function . RESULTS Our program efficiently selected much less number of beams without compromising the desired surface coverage . For example , a set of 17 beams were selected for a pancreas case , covering 92.5 % of the surface area covered by the 94 beams for R=5 cm with 20 % maximal overlap . The IMRT plans with the selected beams show superior quality with dramatically improved critical structure sparing , comparing with the clinically approved IMRT plans . We find that SPG smoothing decreases delivery time by a factor of 2 for the pancreas and lung cases and 2.7 for the prostate . For the pancreas case , we examine collimator rotation . Compared to the default angle of 0 degrees ( i.e. leaf motion perpendicular to gantry rotation direction ) , a well‐chosen single collimator angle ( 23 ° in this case ) can reduce the delivery time by an additional 8 % . Ultrasound imaging is considered to be safer , economical and can image the organs in real-time , which makes it widely used diagnostic imaging modality in health-care . Ultrasound imaging covers the broad spectrum of medical diagnostics ; these include diagnosis of kidney , liver , pancreas , fetal monitoring , etc . Currently , the diagnosis through ultrasound scanning is clinic-centered , and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis . - We are working in collaboration with tumor registry in collection and analysis of data to obtain health results . We have results survival in five processes breast , lung , kidney , pancreas , colorectal and they are similar to other published data . - The fourth mainstay of our work is the healthcare at the end of the life . Yet pancreas is a challenging abdominal organ to segment due to the high inter-patient anatomical variability in both shape and volume metrics . The UNet often suffers from pancreas over-segmentation , under-segmentation and shape inconsistency between the predicted result and ground truth . We consider the UNet can not extract more deepen features and rich semantic information which can not distinguish the regions between pancreas and background . We report 87.57 ± 3.26 % of the mean Dice score , which outperforms the state-of-the-art method , producing 7.87 % improvement from the predicted result of original UNet . Our method is not only superior to the other established methods in terms of accuracy and robustness but can also effectively restrain pancreas over-segmentation , under-segmentation and shape inconsistency between the predicted result and ground truth . Our strategies prone to apply to clinical medicine . The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients . The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction . Accurate pancreas segmentation from medical images is an important yet challenging problem for medical image analysis and medical surgery . Challenges relating to the pancreas image acquisition , the limited availability of image data and segmentation methodology hinder this segmentation task . This paper aims to present a systematic review of the different methodologies . However , pancreas segmentation is challenging because of the high variability in location and anatomy of the organs , while occupying only a very small part of the entire abdominal CT scans . Due to the rapid development of the CAD system and the urgent need for clinical treatment , the pancreas image segmentation with high precision is demanded . In this paper , we propose a new approach for automatic pancreas segmentation of CT images using inter-/intra-slice contextual information with a cascade neural network . This inhibits traditional automated segmentation methods from achieving high accuracies , especially compared to the performance obtained for other organs , such as the liver , heart or kidneys . To fill this gap , we present an automated system from 3D computed tomography ( CT ) volumes that is based on a two‐stage cascaded approach — pancreas localization and pancreas segmentation . For the first step , we localize the pancreas from the entire 3D CT scan , providing a reliable bounding box for the more refined segmentation step . The main challenges for accurate CT pancreas segmentation lie in two aspects : ( 1 ) large shape variation across different patients , and ( 2 ) low contrast and blurring around the pancreas boundary . In this paper , we propose a two-stage , ensemble-based fully convolutional neural network ( FCN ) to solve the challenging pancreas segmentation problem in CT images . First , candidate region generation is performed by classifying patches generated by superpixels . Deep-learning based technology has achieved unimaginable success , but always faces the problem of insufficient labeled data . In this paper , a novel strategy is proposed to automate cerebrovascular segmentation , which integrates model- and data-driven methods . Firstly , the TOF-MRA data are sparsely labeled by three radiologists . In this paper an automatic fuzzy based method for the extraction of the cerebrovascular system from 3D Time-of-Flight ( TOF ) MRA image sequences is presented . In order to exclude non-brain tissue an automatic skull stripping method is applied in a preprocessing step . Stroke is a serious cerebrovascular condition in which brain cells die due to an abrupt blockage of arteries supplying blood and oxygen or when a blood vessel bursts or ruptures and causes bleeding in the brain . Because the onset of stroke is very sudden in most people , prevention is often difficult . PurposeSuperficial temporal artery (STA)—middle cerebral artery ( MCA ) bypass is an important technique for cerebrovascular reconstruction . Intraoperative hemodynamic imaging is needed to perform cerebrovascular reconstruction safely and effectively . We propose a method to extract a three dimensional volume showing either the arteries ( arteriogram ) or the veins ( venogram ) from the 4D CTP scan . This would allow cerebrovascular assessment using the CTP scan and obviate the need for acquiring an additional CTA scan . Preprocessing steps consist of registration of the time volumes of the CTP scan using rigid registration and masking out extracranial structures , bone and air . In this study , we examined whether higher physical fitness levels enhance cognitive and neural plasticity in MS patients . The present study is the first to investigate the impact of cardiorespiratory fitness on cerebrovascular functioning of MS patients . 24 participants with relapsing-remitting MS were recruited for the study . 4D arterial spin labeling magnetic resonance angiography ( 4D ASL MRA ) is a non-invasive and safe modality for cerebrovascular imaging procedures . It uses the patient ’s magnetically labeled blood as intrinsic contrast agent , so that no external contrast media is required . Cerebrovascular pathology is one of the main fatal diseases which seriously affect the human 's health . Extracting the accurate image of cerebral vascular tissue is the key of clinical diagnosis . Conclusions This study confirmed that visual simulation of IA clipping based on the processing of high-resolution 3D imaging can be effective . This is a new and important step toward the development of a more sophisticated integrated simulation platform dedicated to cerebrovascular surgery .
In brain MR images , the boundary of tumor tissue is highly irregular . Deformable models and Region based methods are extensively used for medical image segmentation , to locate the boundary of the tumor . Problems associated with non-linear distribution of real data , User interaction and poor convergence to the boundary region limited their usefulness .
Advanced medical imaging techniques require high performance segmentation algorithms . Extracting the structures of interest accurately is one of the main challenges in medical imaging segmentation . In this paper a methodological approach based on active contour is proposed for fully automated segmentation of radius bone .
Therefore , accurate segmentation of brain images is a very difficult task . In this paper we present an automatic method of brain segmentation for detection of tumor . The MR images from T1 , T2 and flair sequences are used for the study along with axial , coronal and sagitial slices .
So brain image segmentation is very important but most challenging problem among medical images . This paper depicts the different challenges and applications of brain image segmentation which can be extended in many directions of brain images . In this paper we review the different modalities of medical images like MRI , CT , and SPECT etc .
Multi-atlas based label fusionmethods have been successfully used for medical image segmentation . In the field of brain region segmentation , multi-atlas based methods propagate labels from multiple atlases to target image by the similarity between patches in target image and atlases . Most of existing multi-atlas based methods usually use intensity feature , which is hard to capture high-order information in brain images .
A level-set based active contour stage is performed for lesion delineation , using gradient and shape properties combined with previously learned region intensity statistics . In the proposed scheme there is no need for preregistration of an atlas , a common characteristic in brain segmentation schemes . Experimental results on real data are presented .
In the image normalization step , intensity non-uniformity is removed . In the brain segmentation step , we use mathematical morphological operators and masking . The proposed algorithm was tested with twenty 3D MR normal brain image sets .
These algorithms have a mean Dice Similarity Coefficient ( DSC ) of 78.35 % with maximum and minimum DSC as 89.7 % and 56.46 % respectively . The availability of better computation power and large datasets will further enhance the performance of pancreas segmentation and related tasks . The approaches presented in this paper can be easily modified to the multi organ segmentation methods by changing the loss function .
RESULTS Our program efficiently selected much less number of beams without compromising the desired surface coverage . For example , a set of 17 beams were selected for a pancreas case , covering 92.5 % of the surface area covered by the 94 beams for R=5 cm with 20 % maximal overlap . The IMRT plans with the selected beams show superior quality with dramatically improved critical structure sparing , comparing with the clinically approved IMRT plans .
We find that SPG smoothing decreases delivery time by a factor of 2 for the pancreas and lung cases and 2.7 for the prostate . For the pancreas case , we examine collimator rotation . Compared to the default angle of 0 degrees ( i.e. leaf motion perpendicular to gantry rotation direction ) , a well‐chosen single collimator angle ( 23 ° in this case ) can reduce the delivery time by an additional 8 % .
Ultrasound imaging is considered to be safer , economical and can image the organs in real-time , which makes it widely used diagnostic imaging modality in health-care . Ultrasound imaging covers the broad spectrum of medical diagnostics ; these include diagnosis of kidney , liver , pancreas , fetal monitoring , etc . Currently , the diagnosis through ultrasound scanning is clinic-centered , and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis .
- We are working in collaboration with tumor registry in collection and analysis of data to obtain health results . We have results survival in five processes breast , lung , kidney , pancreas , colorectal and they are similar to other published data . - The fourth mainstay of our work is the healthcare at the end of the life .
Yet pancreas is a challenging abdominal organ to segment due to the high inter-patient anatomical variability in both shape and volume metrics . The UNet often suffers from pancreas over-segmentation , under-segmentation and shape inconsistency between the predicted result and ground truth . We consider the UNet can not extract more deepen features and rich semantic information which can not distinguish the regions between pancreas and background .
We report 87.57 ± 3.26 % of the mean Dice score , which outperforms the state-of-the-art method , producing 7.87 % improvement from the predicted result of original UNet . Our method is not only superior to the other established methods in terms of accuracy and robustness but can also effectively restrain pancreas over-segmentation , under-segmentation and shape inconsistency between the predicted result and ground truth . Our strategies prone to apply to clinical medicine .
The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients . The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction .
Accurate pancreas segmentation from medical images is an important yet challenging problem for medical image analysis and medical surgery . Challenges relating to the pancreas image acquisition , the limited availability of image data and segmentation methodology hinder this segmentation task . This paper aims to present a systematic review of the different methodologies .
However , pancreas segmentation is challenging because of the high variability in location and anatomy of the organs , while occupying only a very small part of the entire abdominal CT scans . Due to the rapid development of the CAD system and the urgent need for clinical treatment , the pancreas image segmentation with high precision is demanded . In this paper , we propose a new approach for automatic pancreas segmentation of CT images using inter-/intra-slice contextual information with a cascade neural network .
This inhibits traditional automated segmentation methods from achieving high accuracies , especially compared to the performance obtained for other organs , such as the liver , heart or kidneys . To fill this gap , we present an automated system from 3D computed tomography ( CT ) volumes that is based on a two‐stage cascaded approach — pancreas localization and pancreas segmentation . For the first step , we localize the pancreas from the entire 3D CT scan , providing a reliable bounding box for the more refined segmentation step .
The main challenges for accurate CT pancreas segmentation lie in two aspects : ( 1 ) large shape variation across different patients , and ( 2 ) low contrast and blurring around the pancreas boundary . In this paper , we propose a two-stage , ensemble-based fully convolutional neural network ( FCN ) to solve the challenging pancreas segmentation problem in CT images . First , candidate region generation is performed by classifying patches generated by superpixels .
Deep-learning based technology has achieved unimaginable success , but always faces the problem of insufficient labeled data . In this paper , a novel strategy is proposed to automate cerebrovascular segmentation , which integrates model- and data-driven methods . Firstly , the TOF-MRA data are sparsely labeled by three radiologists .
In this paper an automatic fuzzy based method for the extraction of the cerebrovascular system from 3D Time-of-Flight ( TOF ) MRA image sequences is presented . In order to exclude non-brain tissue an automatic skull stripping method is applied in a preprocessing step .
Stroke is a serious cerebrovascular condition in which brain cells die due to an abrupt blockage of arteries supplying blood and oxygen or when a blood vessel bursts or ruptures and causes bleeding in the brain . Because the onset of stroke is very sudden in most people , prevention is often difficult .
PurposeSuperficial temporal artery (STA)—middle cerebral artery ( MCA ) bypass is an important technique for cerebrovascular reconstruction . Intraoperative hemodynamic imaging is needed to perform cerebrovascular reconstruction safely and effectively .
We propose a method to extract a three dimensional volume showing either the arteries ( arteriogram ) or the veins ( venogram ) from the 4D CTP scan . This would allow cerebrovascular assessment using the CTP scan and obviate the need for acquiring an additional CTA scan . Preprocessing steps consist of registration of the time volumes of the CTP scan using rigid registration and masking out extracranial structures , bone and air .
In this study , we examined whether higher physical fitness levels enhance cognitive and neural plasticity in MS patients . The present study is the first to investigate the impact of cardiorespiratory fitness on cerebrovascular functioning of MS patients . 24 participants with relapsing-remitting MS were recruited for the study .
4D arterial spin labeling magnetic resonance angiography ( 4D ASL MRA ) is a non-invasive and safe modality for cerebrovascular imaging procedures . It uses the patient ’s magnetically labeled blood as intrinsic contrast agent , so that no external contrast media is required .
Cerebrovascular pathology is one of the main fatal diseases which seriously affect the human 's health . Extracting the accurate image of cerebral vascular tissue is the key of clinical diagnosis .
Conclusions This study confirmed that visual simulation of IA clipping based on the processing of high-resolution 3D imaging can be effective . This is a new and important step toward the development of a more sophisticated integrated simulation platform dedicated to cerebrovascular surgery .
Performance Analysis of Fuzzy C Means Algorithm in Automated Detection of Brain Tumor A fully automated segmentation of radius bone based on active contour in wrist MRI data set Local entropy based brain MR image segmentation Methods and Techniques for Brain Image Segmentation High-order boltzmann machine-based unsupervised feature learning for multi-atlas segmentation Improved CSF classification and lesion detection in MR brain images with multiple sclerosis Automated Brain Segmentation Algorithm for 3D Magnetic Resonance Brain Images A Literature Review on Application of Machine Learning Techniques in Pancreas Segmentation SU-E-T-591: Geometry-Based Non-Coplanar Beam Selection for Intensity Modulated Robotic Radiotherapy. TU‐A‐BRB‐01: Convex Optimization Techniques for Handling Delivery Time and Plan Complexity in Multi‐Criteria VMAT Optimization Computer-Assisted Algorithms for Ultrasound Imaging Systems Integrating Health Care in Cancer. Our experience in OSI Donostialdea Multiscale receptive field based on residual network for pancreas segmentation in CT images A Hybrid Method for Pancreas Extraction from CT Image Based on Level Set Methods Advances on pancreas segmentation: a review Pancreas Segmentation in Abdominal CT Scans using Inter-/Intra-Slice Contextual Information with a Cascade Neural Network Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆ Automatic Pancreas Segmentation via Coarse Location and Ensemble Learning Cerebrovascular segmentation from TOF-MRA using model- and data-driven method via sparse labels Fuzzy-Based Extraction of Vascular Structures from Time-of-Flight MR Images Predicting Cerebral Aneurysm Rupture by Gradient Boosting Decision Tree using Clinical, Hemodynamic, and Morphological Information Intraoperative visualization of cerebral oxygenation using hyperspectral image data: a two-dimensional mapping method Segmentation of arteries and veins on 4D CT perfusion scans for constructing arteriograms and venograms Cardiorespiratory fitness: A predictor of cortical plasticity in multiple sclerosis Vessel segmentation in 4D arterial spin labeling magnetic resonance angiography images of the brain A vessel segmentation method for serialized cerebralvascular DSA images based on spatial feature point set of rotating coordinate system Preoperative Simulation for the Planning of Microsurgical Clipping of Intracranial Aneurysms
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ISBI Medical Imaging: Image Processing EMBC MIE CATA Medical Imaging: Image Processing Medical Imaging: Computer-Aided Diagnosis
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