varunr14/newsummary huggingface dataset:
Conventional in room cone beam computed tomography lacks explicit representation of patient respiratory motion and usually has poor image quality and inaccurate CT numbers for target delineation and or adaptive treatment planning . In room four dimensional CBCT image acquisition is still time consuming and suffers the same issue of poor image quality . To overcome this limitation we developed a computational framework to digitally synthesize high quality daily 4D CBCT images using the prior knowledge of motion and appearance learned from the planning 4D CT dataset . A patient specific respiratory motion model was first constructed from the planning 4D CT images using principal component analysis of displacement vector fields across different respiratory phases . Subsequently the respiratory motion model as well as the image content of the planning CT was spatially mapped onto the daily CBCT using deformable image registration . The synthesized 4D images possess explicit patient motion while maintaining the geometric accuracy of patient s anatomy at the time of treatment . We validated our model by quantitatively comparing the synthesized 4D CBCT against the 4D CT dataset acquired in the same day from protocol patients undergoing daily in room CBCT setup and weekly 4D CT for treatment evaluation . Our preliminary results have demonstrated good agreement of contours in different motion phases between the synthesized and acquired scans . Various imaging artifacts were also suppressed and soft tissue visibility was enhanced . 


Summary: By taking advantages of deformable image registration we can successfully convert a single a low quality cone beam CT into a high quality multi phase cone beam CT images.
Volume visualization is a very important work in medical imaging and surgery plan . However determining an ideal transfer function is still a challenging task because of the lack of measurable metrics for quality of volume visualization . In the paper we presented the voxel vibility model as a quality metric to design the desired visibility for voxels instead of designing transfer functions directly . Transfer functions are obtained by minimizing the distance between the desired visibility distribution and the actual visibility distribution . The voxel model is a mapping function from the feature attributes of voxels to the visibility of voxels . To consider between class information and with class information simultaneously the voxel visibility model is described as a Gaussian mixture model . To highlight the important features the matched result can be obtained by changing the parameters in the voxel visibility model through a simple and effective interface . Simultaneously we also proposed an algorithm for transfer functions optimization . The effectiveness of this method is demonstrated through experimental results on several volumetric data sets . 


Summary: We propose the voxel visibility model for transfer function in volume rendering. We propose an optimization algorithm for automatic transfer function design. The voxel visibility model is a feature and voxel level model. The voxel visibility model can be efficiently used by users. The voxel visibility model provides an importance based strategy.
We propose a novel 3D 2D registration approach for micro computed tomography and histology constructed for dental implant biopsies that finds the position and normal vector of the oblique slice from CT that corresponds to HI . During image pre processing the implants and the bone tissue are segmented using a combination of thresholding morphological filters and component labeling . After this chamfer matching is employed to register the implant edges and fine registration of the bone tissues is achieved using simulated annealing . The method was tested on n 10 biopsies obtained at 20 weeks after non submerged healing in the canine mandible . The specimens were scanned with CT 100 and processed for hard tissue sectioning . After registration we assessed the agreement of bone to implant contact using automated and manual measurements . Statistical analysis was conducted to test the agreement of the BIC measurements in the registered samples . Registration was successful for all specimens and agreement of the respective binary images was high . Direct comparison of BIC yielded that automated and manual measures from CT were significant positively correlated with HI between CT and HI groups . The results show that this method yields promising results and that CT may become a valid alternative to assess osseointegration in three dimensions . 


Summary: We developed a novel method for automated 3D 2D registration of microCT and histology. We used the model of dental implant biopsies and found high agreement among the registered specimens. We directly compared the bone to implant contact BIC using the registered samples. We found significant positive association for the BIC measurements. Metal artifacts increased automated BIC measurements systematically.
Prostate cancer is one of the major causes of cancer death for men . Magnetic resonance imaging is being increasingly used as an important modality to localize prostate cancer . Therefore localizing prostate cancer in MRI with automated detection methods has become an active area of research . Many methods have been proposed for this task . However most of previous methods focused on identifying cancer only in the peripheral zone or classifying suspicious cancer ROIs into benign tissue and cancer tissue . Few works have been done on developing a fully automatic method for cancer localization in the entire prostate region including central gland and transition zone . In this paper we propose a novel learning based multi source integration framework to directly localize prostate cancer regions from in vivo MRI . We employ random forests to effectively integrate features from multi source images together for cancer localization . Here multi source images include initially the multi parametric MRIs and later also the iteratively estimated and refined tissue probability map of prostate cancer . Experimental results on 26 real patient data show that our method can accurately localize cancerous sections . The higher section based evaluation combined with the ROC analysis result of individual patients shows that the proposed method is promising for in vivo MRI based prostate cancer localization which can be used for guiding prostate biopsy targeting the tumor in focal therapy planning triage and follow up of patients with active surveillance as well as the decision making in treatment selection . The common ROC analysis with the AUC value of 0.832 and also the ROI based ROC analysis with the AUC value of 0.883 both illustrate the effectiveness of our proposed method . 


Summary: We propose an automatic detection method to localize prostate cancer in MRI. We localize prostate cancer in peripheral zone PZ as well as in central gland CG and transition zone TZ . The random forest is employed to effectively integrate features from multi source images. Experimental results show that our method can accurately localize the cancerous sections. The results of our proposed method are better than that of other four conventional methods.
Diabetes increases the risk of developing any deterioration in the blood vessels that supply the retina an ailment known as Diabetic Retinopathy . Since this disease is asymptomatic it can only be diagnosed by an ophthalmologist . However the growth of the number of ophthalmologists is lower than the growth of the population with diabetes so that preventive and early diagnosis is difficult due to the lack of opportunity in terms of time and cost . Preliminary affordable and accessible ophthalmological diagnosis will give the opportunity to perform routine preventive examinations indicating the need to consult an ophthalmologist during a stage of non proliferation . During this stage there is a lesion on the retina known as microaneurysm which is one of the first clinically observable lesions that indicate the disease . In recent years different image processing algorithms which allow the detection of the DR have been developed however the issue is still open since acceptable levels of sensitivity and specificity have not yet been reached preventing its use as a pre diagnostic tool . Consequently this work proposes a new approach for MA detection based on reduction of non uniform illumination normalization of image grayscale content to improve dependence of images from different contexts application of the bottom hat transform to leave reddish regions intact while suppressing bright objects binarization of the image of interest with the result that objects corresponding to MAs blood vessels and other reddish objects are completely separated from the background application of the hit or miss Transformation on the binary image to remove blood vessels from the ROIs two features are extracted from a candidate to distinguish real MAs from FPs where one feature discriminates round shaped candidates from elongated shaped ones through application of Principal Component Analysis the second feature is a count of the number of times that the radon transform of the candidate ROI evaluated at the set of discrete angle values 0 1 2 180 is characterized by a valley between two peaks . The proposed approach is tested on the public databases DiaretDB1 and Retinopathy Online Challenge competition . The proposed MA detection method achieves sensitivity specificity and precision of 92.32 93.87 and 95.93 for the diaretDB1 database and 88.06 97.47 and 92.19 for the ROC database . Theory results challenges and performance related to the proposed MA detecting method are presented . 


Summary: A new approach for microaneurysm detection is tested in two public image databases. Normalization of grayscale content is an essential preprocessing stage. Coarse segmentation for candidate selection is based on morphologic processing. Principal component analysis and Radon transform are used during feature extraction. The number of features to extract is very small with only two features.
The automatization of the analysis of Indirect Immunofluorescence images is of paramount importance for the diagnosis of autoimmune diseases . This paper proposes a solution to one of the most challenging steps of this process the segmentation of HEp 2 cells through an adaptive marker controlled watershed approach . Our algorithm automatically conforms the marker selection pipeline to the peculiar characteristics of the input image hence it is able to cope with different fluorescent intensities and staining patterns without any a priori knowledge . Furthermore it shows a reduced sensitivity to over segmentation errors and uneven illumination that are typical issues of IIF imaging . 


Summary: We propose automated segmentation of HEp 2 cells in immunofluorescence imaging. We apply the same pipeline to images with different fluorescent pattern and intensity. Our segmentation approach is based on adaptive marker controlled watershed. We assess the accuracy of our approach on a public dataset. We compare our performance with significant works from literature.
A computerized framework for monitoring four dimensional dose distributions during stereotactic body radiation therapy based on a portal dose image based 2D 3D registration approach has been proposed in this study . Using the PDI based registration approach simulated 4D treatment CT images were derived from the deformation of 3D planning CT images so that a 2D planning PDI could be similar to a 2D dynamic clinical PDI at a breathing phase . The planning PDI was calculated by applying a dose calculation algorithm to the geometry of the planning CT image and a virtual water equivalent phantom . The dynamic clinical PDIs were estimated from electronic portal imaging device dynamic images including breathing phase data obtained during a treatment . The parameters of the affine transformation matrix were optimized based on an objective function and a gamma pass rate using a Levenberg Marquardt algorithm . The proposed framework was applied to the EPID dynamic images of ten lung cancer patients which included 183 frames . The 4D dose distributions during the treatment time were successfully obtained by applying the dose calculation algorithm to the simulated 4D treatment CT images . The mean standard deviation of the percentage errors between the prescribed dose and the estimated dose at an isocenter for all cases was 3.25 4.43 . The maximum error for the ten cases was 14.67 and the minimum error was 0.00 . The proposed framework could be feasible for monitoring the 4D dose distribution and dose errors within a patient s body during treatment . 


Summary: We have investigated a framework for monitoring 4D dose distributions. A portal dose image based 2D 3D registration has been proposed. Dose errors can be calculated to ensure the quality of the radiation therapy.
This paper proposes a QoS active queue management mechanism for multi QoS classes named as M GREEN which includes the consideration of QoS parameters and provides service differentiation among different flows classes . M GREEN extends the concept of Random and Early Detection in RED to Global Random and Early Estimation respectively . Furthermore M GREEN extends the linear concept of RED to an exponential one to enhance the efficiency of AQM . For performance evaluation extensive numerical cases are employed to compare M GREEN with some popular AQM schemes and to show the superior performance and characteristics of M GREEN . Consequently M GREEN is a possible way to provide the future multimedia Internet with differential services for different traffic classes of diverse QoS requirements . 


Summary: We proposes a QoS AQM mechanism M GREEN for multi QoS classes. It extends the concept of Random in RED to Global Random . It extends the concept of Early Detection in RED to Early Estimation . It extends the linear concept of RED to an exponential one.
Shape based 3D surface reconstructing methods for liver vessels have difficulties to tackle with limited contrast of medical images and the intrinsic complexity of multi furcation parts . In this paper we propose an effective and robust technique called Gap Border Pairing to reconstruct surface of liver vessels with complicated multi furcations . The proposed method starts from a tree like skeleton which is extracted from segmented liver vessel volumes and preprocessed as a number of simplified smooth branching lines . Secondly for each center point of any branching line an optimized elliptic cross section ring is generated by optimizedly fitting its actual cross section outline based on its tangent vector . Thirdly a tubular surface mesh is generated for each branching line by weaving all of its adjacent rings . Then for every multi furcation part a transitional regular mesh is effectively and regularly reconstructed by using GBP . An initial model is generated after reconstructing all multi furcation parts . Finally the model is refined by using just one time subdivision and its topologies can be re maintained by grouping its facets according to the skeleton providing high level editability . Our method can be automatically implemented in parallel if the segmented vessel volume and corresponding skeletons are provided . The experimental results show that GBP model is accurate enough in terms of the boundary deviations between segmented volume and the model . 


Summary: Traditional methods for reconstructing complicated multi furcation are limited. Turing points are yield on borders if three branches intersect with each other. Turning points having anisotropic opposite relationship exactly make a triangle. Any border segment can pair one and only opposite segment. Topology of refined surface can be re maintained by checking original vertices.
Many organizations are implementing process improvement models seeking to increase their organizational maturity for software development . However implementing traditional maturity models involves a large investment which is beyond the reach of vast majority of small organizations . This paper presents the use and adaptation of some ISO models in the creation of an organizational maturity model for the Spanish software industry . This model was used satisfactorily to improve the software processes of several Spanish small firms and obtain an organizational maturity certification for software development granted by the Spanish Association for Standardization and Certification . 


Summary: A Software Engineering Maturity Model was produced for Spanish software industry. The model is based on standards ISO IEC 12207 ISO IEC 15504 and ISO IEC 17021. It allows a certification of the organizational maturity for software enterprises. Helping to improve the software development quality in all types of enterprises. Currently there are 38 development enterprises certified by AENOR in this model.
Colorectal cancer usually appears in polyps developed from the mucosa . Carcinoma is frequently found in those polyps larger than 10mm and therefore only this kind of polyps is sent for pathology examination . In consequence accurate estimation of a polyp size determines the surveillance interval after polypectomy . The follow up consists in a periodic colonoscopy whose frequency depends on the estimation of the size polyp . Typically this polyp measure is achieved by examining the lesion with a calibrated endoscopy tool . However measurement is very challenging because it must be performed during a procedure subjected to a complex mix of noise sources namely anatomical variability drastic illumination changes and abrupt camera movements . This work introduces a semi automatic method that estimates a polyp size by propagating an initial manual delineation in a single frame to the whole video sequence using a spatio temporal characterization of the lesion during a routine endoscopic examination . The proposed approach achieved a Dice Score of 0.7 in real endoscopy video sequences when comparing with an expert . In addition the method obtained a root mean square error of 0.87mm in videos artificially captured in a cylindric structure with spheres of known size that simulated the polyps . Finally in real endoscopy sequences the diameter estimation was compared with measures obtained by a group of four experts with similar experience obtaining a RMSE of 4.7mm for a set of polyps measuring from 5 to 20mm . An ANOVA test performed for the five groups of measurements showed no significant differences . 


Summary: The approach segments a polyp by propagating an initial manual delineation. Polyp segmentation is performed by a combination of motion and appearance features. A defocus strategy estimates the camera distance and the actual polyp size. Four experts and the method did not show significant differences in endoscopy. The approach is robust to different types of noise and can be used in real scenarios.
Mitral valve diseases are among the most common types of heart diseases while heart diseases are the most common cause of death worldwide . MV repair surgery is connected to higher survival rates and fewer complications than the total replacement of the MV but MV repair requires extensive patient specific therapy planning . The simulation of MV repair with a patient specific model could help to optimize surgery results and make MV repair available to more patients . However current patient specific simulations are difficult to transfer to clinical application because of time constraints or prohibitive requirements on the resolution of the image data . As one possible solution to the problem of patient specific MV modeling we present a mass spring MV model based on 3D transesophageal echocardiographic images already routinely acquired for MV repair therapy planning . Our novel approach to the rest length estimation of springs allows us to model the global support of the MV leaflets through the chordae tendinae without the need for high resolution image data . The model is used to simulate MV annuloplasty for five patients undergoing MV repair and the simulated results are compared to post surgical TEE images . The comparison shows that our model is able to provide a qualitative estimate of annuloplasty surgery . In addition the data suggests that the model might also be applied to simulating the implantation of artificial chordae . 


Summary: We present a mass spring model for simulating mitral valve repair. The patient specific model geometry is based on pre surgical 3D TEE images. We use a rest length estimation algorithm for modeling of the tendinous cords. A comparison with post surgical 3D TEE images proves the feasibility of our approach. Our simulation is faster than comparable state of the art finite element simulations.
The present research was directed to effective image restoration with the extraction of ischemic edema signs . Computerized support of hyperacute stroke diagnosis based on routinely used computerized tomography scans was optimized to visualize the infarct extent more precisely . In particular a beneficial support of time limited appropriate decision of whether to treat the patient by thrombolysis is expected . Because of a limited accuracy in determining the area of core infarction particularly in the early hours of symptoms onset a variational approach to sensed data recovery was applied . Proposed methodology adjusts fidelity norms and regularization priors integrated with simulated sensing procedures in a compressed sensing framework . Experimental study confirmed almost perfect recognition of ischemic stroke in a test set of over 500 CT scans . 


Summary: We designed and verified computerized support of acute stroke diagnosis. Novelty is application of integrated variational approach to detect ischemic edema. New content oriented image restoration and descriptors of asymmetric hypodensity distribution were proposed. Clarified visualization of direct symptoms of pathology was completed with automated recognition of ischemic stroke. Achieved recognition accuracy was 1 for test set of over 500 CT scans.