scholarly journals Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation

PLoS ONE ◽  
2017 ◽  
Vol 12 (6) ◽  
pp. e0178944 ◽  
Author(s):  
Stephen S. F. Yip ◽  
Chintan Parmar ◽  
Daniel Blezek ◽  
Raul San Jose Estepar ◽  
Steve Pieper ◽  
...  
2019 ◽  
Vol 92 (1104) ◽  
pp. 20190470 ◽  
Author(s):  
Craig Steven Moore ◽  
Tim Wood ◽  
Ged Avery ◽  
Steve Balcam ◽  
Liam Needler ◽  
...  

Objective: The aim of this study was to investigate via computer simulation a proposed improvement to clinical practice by deriving an optimized tube voltage (kVp) range for digital radiography (DR) chest imaging. Methods: A digitally reconstructed radiograph algorithm was used which was capable of simulating DR chest radiographs containing clinically relevant anatomy. Five experienced image evaluators graded clinical image criteria, i.e. overall quality, rib, lung, hilar, spine, diaphragm and lung nodule in images of 20 patients at tube voltages across the diagnostic energy range. These criteria were scored against corresponding images of the same patient reconstructed at a specific reference kVp. Evaluators were blinded to kVp. Evaluator score for each criterion was modelled with a linear mixed effects algorithm and compared with the score for the reference image. Results: Score was dependent on tube voltage and image criteria in a statistically significant manner for both. Overall quality, hilar, diaphragm and spine criteria performed poorly at low and high tube voltages, peaking at 80–100 kVp. Lung and lung nodule demonstrated little variation. Rib demonstrated superiority at low kVp. Conclusion: A virtual clinical trial has been performed with simulated chest DR images. Results indicate mid-range tube voltages of 80–100 kVp are optimum for average adults. Advances in knowledge: There are currently no specific recommendations for optimized tube voltage parameters for DR chest imaging. This study, validated with images containing realistic anatomical noise, has investigated and recommended an optimal tube voltage range.


Author(s):  
Hina Shakir

Lung nodule segmentation in CT images and its subsequent volume analysis can help determinethe malignancy status of a lung nodule. While several efficient segmentation schemes have beenproposed, only a few studies evaluated the segmentation’s performance for large nodules. In thisresearch, we contribute a semi-automatic system which is capable of performing robust 3-D segmen-tations on both small and large nodules with good accuracy. The target CT volume is de-noisedwith an anisotropic diffusion filter and a region of interest is selected around the target nodule ona reference slice. The proposed model performs nodule segmentation by incorporating a mean in-tensity based threshold in Geodesic Active Contour model in level sets. We also devise an adaptivetechnique using image intensity histogram to estimate the desired mean intensity of the nodule.The proposed system is validated on both lung nodules and phantoms collected from publicly avail-able diverse databases. Quantitative and visual comparative analysis of the proposed work withthe Chan-Vese algorithm and statistic active contour model of 3D Slicer platform is also presented.The resulting mean spatial overlap between segmented nodules and reference nodules is 0.855, themean volume bias is 0.10±0.2 ml and the algorithm repeatability is 0.060 ml. The achieved resultssuggest that the proposed method can be used for volume estimations of small as well as large-sizednodules.


2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xixi Guo ◽  
Yuze Li ◽  
Chunjie Yang ◽  
Yanjiang Hu ◽  
Yun Zhou ◽  
...  

This study aimed to detect and diagnose the lung nodules as early as possible to effectively treat them, thereby reducing the burden on the medical system and patients. A lung computed tomography (CT) image segmentation algorithm was constructed based on the deep learning convolutional neural network (CNN). The clinical data of 69 patients with lung nodules diagnosed by needle biopsy and pathological comprehensive diagnosis at hospital were collected for specific analysis. The CT image segmentation algorithm was used to distinguish the nature and volume of lung nodules and compared with other computer aided design (CAD) software (Philips ISP). 69 patients with lung nodules were treated by radiofrequency ablation (RFA). The results showed that the diagnostic sensitivity of the CT image segmentation algorithm based on the CNN was obviously higher than that of the Philips ISP for solid nodules <5 mm (63 cases vs. 33 cases) ( P < 0.05 ); it was the same result for the subsolid nodule <5 mm (33 case vs. 5 cases) ( P < 0.05 ) that was slightly higher for solid and subsolid nodules with a diameter of 5–10 mm (37 cases vs. 28 cases) ( P < 0.05 ). In addition, the CNN algorithm can reach all detection for calcified nodules and pleural nodules (7 cases; 5 cases), and the diagnostic sensitivities were much better than those of Philips ISP (2 cases; 3 cases) ( P < 0.05 ). Patients with pulmonary nodules treated by RFA were in good postoperative condition, with a half-year survival rate of 100% and a one-year survival rate of 72.4%. Therefore, it could be concluded that the CT image segmentation algorithm based on the CNN could effectively detect and diagnose the lung nodules early, and the RFA could effectively treat the lung nodules.


2016 ◽  
Vol 01 (01) ◽  
pp. 1640003 ◽  
Author(s):  
Franklin King ◽  
Jagadeesan Jayender ◽  
Sharath K. Bhagavatula ◽  
Paul B. Shyn ◽  
Steve Pieper ◽  
...  

Purpose: Advancements in and adoption of consumer virtual reality (VR) are currently being propelled by numerous upcoming devices such as the Oculus Rift. Although applications are currently growing around the entertainment field, wide-spread adoption of VR devices opens up the potential for other applications that may have been unfeasible with past implementations of VR. A VR environment may provide an equal or larger screen area than what is provided with the use of multiple conventional displays while remaining comparatively cheaper and more portable making it an attractive option for diagnostic radiology applications. Methods A VR application for the viewing of multiple image slices was designed using: the Oculus Rift head-mounted display (HMD), Unity, and 3D Slicer. Volumes loaded within 3D Slicer are sent to a Unity application that proceeds to render a scene for the Oculus Rift HMD. Users may interact with the images adjusting windowing and leveling using a handheld gamepad controller. Multiple images may be brought closer to the user for detailed inspection. Results Application usage was demonstrated with the simultaneous visualization of longitudinal slices of a serial CT scan of a patient with a lung nodule. Pilot studies for validating usage of the VR system for differential diagnosis and remote collaboration were performed. Initial results suggest that using the VR system increased both task load and time taken to complete tasks, however, the resulting accuracy in assessing nodule growth of nodules was not significantly different than that achieved using a DICOM viewer application on a traditional display.


2020 ◽  
Vol 3 (3) ◽  
pp. 297-310 ◽  
Author(s):  
Rafael Ricafranca Castillo ◽  
Gino Rei A. Quizon ◽  
Mario Joselito M. Juco ◽  
Arthur Dessi E. Roman ◽  
Donnah G De Leon ◽  
...  

 Treatment for coronavirus disease 2019 (COVID19) pneumonia remains empirical and the search for therapies that can improve outcomes continues. Melatonin has been shown to have anti-inflammatory, antioxidant, and immune-modulating effects that may address key pathophysiologic mechanisms in the development and progression of acute respiratory distress syndrome (ARDS), which has been implicated as the likely cause of death in COVID19. We aimed to describe the observable clinical outcomes and tolerability of high-dose melatonin (hdM) given as adjuvant therapy in patients admitted with COVID19 pneumonia. We conducted a retrospective descriptive case series of patients who: 1) were admitted to the Manila Doctors Hospital in Manila, Philippines, between March 5, 2020 and April 4, 2020; 2) presented with history of typical symptoms (fever, cough, sore throat, loss of smell and/or taste, myalgia, fatigue); 3) had admitting impression of atypical pneumonia; 4) had history and chest imaging findings highly suggestive of COVID19 pneumonia, and, 5) were given hdM as adjuvant therapy, in addition to standard and/or empirical therapy. One patient admitted to another hospital, who one of the authors helped co-manage, was included. He was the lone patient given hdM in that hospital during the treatment period. Main outcomes described were: time to clinical improvement, duration of hospital stay from hdM initiation, need for mechanical ventilation (MV) prior to cardiopulmonary resuscitation, and final outcome (death or recovery/discharge). Of 10 patients given hdM at doses of 36-72mg/day per os (p.o.) in 4 divided doses as adjuvant therapy, 7 were confirmed COVID19 positive (+) by reverse transcription polymerase chain reaction (RT-PCR) and 3 tested negative  (-), which was deemed to be false (-) considering the patients’ typical history, symptomatology, chest imaging findings and elevated bio-inflammatory parameters.  In all 10 patients given hdM, clinical stabilization and/or improvement was noted within 4-5 days after initiation of hdM. All hdM patients, including 3 with moderately severe ARDS and 1 with mild ARDS, survived; none required MV. The 7 COVID19(+) patients were discharged at an average of 8.6 days after initiation of hdM. The 3 highly probable COVID19 patients on hdM were discharged at an average of 7.3 days after hdM initiation. Average hospital stay of those not given hdM (non-hdM) COVID19(+) patients who were admitted during the same period and recovered was 13 days. To provide perspective, although the groups are not comparable, 12 of the 34 (35.3%) COVID19(+) non-hdM patients admitted during the same period died, 7/34 (20.6%) required MV; while 6 of 15 (40%) non-hdM (-) by RT-PCR but highly probable COVID19 pneumonia patients also died, 4/15  (26.7%) required MV. No significant side-effects were noted with hdM except for sleepiness, which was deemed favorable by all patients, most of whom had anxiety- and symptom-related sleeping problems previously. HdM may have a beneficial role in patients treated for COVID19 pneumonia, in terms of shorter time to clinical improvement, less need for MV, shorter hospital stay, and possibly lower mortality. HdM was well tolerated. This is the first report describing the benefits of hdM in patients being treated for COVID19 pneumonia.  Being a commonly available and inexpensive sleep-aid supplement worldwide, melatonin may play a role as adjuvant therapy in the global war against COVID19. 


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