Example of International Journal of Computer Assisted Radiology and Surgery format

Sample paper formatted on SciSpace - SciSpace

This content is only for preview purposes. The original open access content can be found here.

Open Access

Recommended

International Journal of Computer Assisted Radiology and Surgery — Template for authors

Publisher: Springer

Your data will always be in your control. Learn more

Get started with a free account

— or sign up using email —

See how it works

Guideline source

Categories	Rank	Trend in last 3 yrs
Surgery	#37 of 422	up by 66 ranks
Radiology, Nuclear Medicine and Imaging	#51 of 288	up by 51 ranks
Health Informatics	#21 of 95	up by 4 ranks
Computer Graphics and Computer-Aided Design	#20 of 88	up by 2 ranks
Computer Science Applications	#162 of 693	up by 37 ranks
Computer Vision and Pattern Recognition	#25 of 85	up by 1 rank
Biomedical Engineering	#72 of 229	up by 10 ranks

Journal quality:

High

Last 4 years overview: 769 Published Papers | 4067 Citations

Indexed in: Scopus

Last updated: 20/07/2020

Related journals

Insights

General info

Related Journals

Open Access

Recommended

Computerized Medical Imaging and Graphics

Elsevier

Quality:

High

CiteRatio: 8.8

SJR: 1.033

SNIP: 1.943

Open Access

Recommended

Medical Image Analysis

Elsevier

Quality:

High

CiteRatio: 24.2

SJR: 2.887

SNIP: 5.246

Open Access

Recommended

Visualization in Engineering

Springer

Quality:

High

CiteRatio: 8.6

SJR: 0.53

SNIP: 2.363

Open Access

Journal of Healthcare Engineering

Hindawi

Quality:

High

CiteRatio: 4.6

SJR: 0.509

SNIP: 1.422

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

2.473

15% from 2018

Impact factor for International Journal of Computer Assisted Radiology and Surgery from 2016 - 2019
Year	Value
2019	2.473
2018	2.155
2017	1.961
2016	1.863

Graph view

5.3

18% from 2019

CiteRatio for International Journal of Computer Assisted Radiology and Surgery from 2016 - 2020
Year	Value
2020	5.3
2019	4.5
2018	3.8
2017	3.1
2016	2.8

Graph view

Insights

Impact factor of this journal has increased by 15% in last year.
This journal’s impact factor is in the top 10 percentile category.

Insights

CiteRatio of this journal has increased by 18% in last years.
This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.701

3% from 2019

SJR for International Journal of Computer Assisted Radiology and Surgery from 2016 - 2020
Year	Value
2020	0.701
2019	0.679
2018	0.625
2017	0.614
2016	0.565

Graph view

1.408

19% from 2019

SNIP for International Journal of Computer Assisted Radiology and Surgery from 2016 - 2020
Year	Value
2020	1.408
2019	1.188
2018	1.183
2017	1.195
2016	1.286

Graph view

Insights

SJR of this journal has increased by 3% in last years.
This journal’s SJR is in the top 10 percentile category.

Insights

SNIP of this journal has increased by 19% in last years.
This journal’s SNIP is in the top 10 percentile category.

International Journal of Computer Assisted Radiology and Surgery

Guideline source: View

All company, product and service names used in this website are for identification purposes only. All product names, trademarks and registered trademarks are property of their respective owners.

Use of these names, trademarks and brands does not imply endorsement or affiliation. Disclaimer Notice

Springer

International Journal of Computer Assisted Radiology and Surgery

The International Journal for Computer Assisted Radiology and Surgery (IJCARS) is a peer-reviewed journal to provide a platform closing the gap between medical and technical disciplines and to encourage interdisciplinary research and development activities in an international environment. The focus of the journal is on R&D areas relating to digital imaging methods and computer-assisted diagnostic and therapeutic workflows that match and enhance the skill levels of health care professionals. IJCARS invites submissions on topics in information sciences, ICT, and mechatronic systems design as well as clinical application fields such as: • Medical imaging, e.g. CT, MR, US, SPECT, PET, DR, molecular imaging, and virtual endoscopy • Image processing and display • 3D, 4D, and 5D imaging • Hospital-wide PACS and telemedicine • Computer applications for e.g. neurosurgery, head and neck, orthopaedics, ear nose and throat, cardiovascular and thoracoabdominal surgery, and plastic/reconstructive surgery • Image-guided therapy • Surgical robotics and instrumentation • Surgical navigation • 3D modeling and rapid prototyping • Postoperative result assessment • Surgical education and training • Haptics and multimodal devices in medical applications • Methods of validation and verification • CAD for breast, prostate, chest, colon, skeletal, liver, brain, and vascular imaging • Cranial and maxillofacial image-guided surgery • Surgical workflow • Surgical DICOM and IHE • Digital operating room IJCARS will present results from these fields in original papers, review articles, short communications and commentaries. To increase the value of health care for the general populace, one focus of IJCARS is on providing information on new diagnostic and therapeutic processes which are facilitated by CARS. This includes providers' experiences, patient outcomes, economic and ethical considerations, and scientific/medical validation results. It is expected that the resulting awareness by users and providers will accelerate the acceptance of CARS-supported procedures in clinical practice. Read Less

Last updated on	20 Jul 2020
ISSN	1861-6410
Impact Factor	High - 1.194
Open Access	No
Sherpa RoMEO Archiving Policy	Green
Plagiarism Check	Available via Turnitin
Endnote Style	Download Available
Bibliography Name	SPBASIC
Citation Type	Author Year (Blonder et al, 1982)
Bibliography Example	Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article • DOI: 10.1007/S11548-016-1467-3 •

Automatic 3D liver location and segmentation via convolutional neural network and graph cut

Fang Lu¹, Fa Wu¹, •

01 Feb 2017 - International Journal of Computer Assisted Radiology and Surgery

Abstract:

Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automa... Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans. The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map. The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively. The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method. read more

Topics:

Cut (56%)56% related to the paper, Segmentation (51%)51% related to the paper

261 Citations

Open access • Journal Article • DOI: 10.1007/S11548-016-1483-3 •

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.

Mohammadreza Soltaninejad¹, Guang Yang², •

01 Feb 2017 - International Journal of Computer Assisted Radiology and Surgery

Abstract:

We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). The method is based on superpixel technique and classification of each superpixel. A number of nove... We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. read more

View PDF

246 Citations

Journal Article • DOI: 10.1007/S11548-017-1605-6 •

Pulmonary nodule classification with deep residual networks

Aiden Nibali¹, Zhen He¹, •

13 May 2017 - International Journal of Computer Assisted Radiology and Surgery

Abstract:

Lung cancer has the highest death rate among all cancers in the USA. In this work we focus on improving the ability of computer-aided diagnosis (CAD) systems to predict the malignancy of nodules from cropped CT images of lung nodules. We evaluate the effectiveness of very deep convolutional neural networks at the task of expe... Lung cancer has the highest death rate among all cancers in the USA. In this work we focus on improving the ability of computer-aided diagnosis (CAD) systems to predict the malignancy of nodules from cropped CT images of lung nodules. We evaluate the effectiveness of very deep convolutional neural networks at the task of expert-level lung nodule malignancy classification. Using the state-of-the-art ResNet architecture as our basis, we explore the effect of curriculum learning, transfer learning, and varying network depth on the accuracy of malignancy classification. Due to a lack of public datasets with standardized problem definitions and train/test splits, studies in this area tend to not compare directly against other existing work. This makes it hard to know the relative improvement in the new solution. In contrast, we directly compare our system against two state-of-the-art deep learning systems for nodule classification on the LIDC/IDRI dataset using the same experimental setup and data set. The results show that our system achieves the highest performance in terms of all metrics measured including sensitivity, specificity, precision, AUROC, and accuracy. The proposed method of combining deep residual learning, curriculum learning, and transfer learning translates to high nodule classification accuracy. This reveals a promising new direction for effective pulmonary nodule CAD systems that mirrors the success of recent deep learning advances in other image-based application domains. read more

Topics:

Deep learning (52%)52% related to the paper

191 Citations

Open access • Journal Article • DOI: 10.1007/S11548-018-1860-1 •

Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery

Ziheng Wang¹, Ann Majewicz Fey¹, •

25 Sep 2018 - International Journal of Computer Assisted Radiology and Surgery

Abstract:

With the advent of robot-assisted surgery, the role of data-driven approaches to integrate statistics and machine learning is growing rapidly with prominent interests in objective surgical skill assessment. However, most existing work requires translating robot motion kinematics into intermediate features or gesture segments ... With the advent of robot-assisted surgery, the role of data-driven approaches to integrate statistics and machine learning is growing rapidly with prominent interests in objective surgical skill assessment. However, most existing work requires translating robot motion kinematics into intermediate features or gesture segments that are expensive to extract, lack efficiency, and require significant domain-specific knowledge. We propose an analytical deep learning framework for skill assessment in surgical training. A deep convolutional neural network is implemented to map multivariate time series data of the motion kinematics to individual skill levels. We perform experiments on the public minimally invasive surgical robotic dataset, JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). Our proposed learning model achieved competitive accuracies of 92.5%, 95.4%, and 91.3%, in the standard training tasks: Suturing, Needle-passing, and Knot-tying, respectively. Without the need of engineered features or carefully tuned gesture segmentation, our model can successfully decode skill information from raw motion profiles via end-to-end learning. Meanwhile, the proposed model is able to reliably interpret skills within a 1–3 second window, without needing an observation of entire training trial. This study highlights the potential of deep architectures for efficient online skill assessment in modern surgical training. read more

Topics:

Deep learning (56%)56% related to the paper, Convolutional neural network (51%)51% related to the paper

171 Citations

Journal Article • DOI: 10.1007/S11548-016-1501-5 •

Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets

Peijun Hu¹, Fa Wu¹, •

01 Mar 2017 - International Journal of Computer Assisted Radiology and Surgery

Abstract:

Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensi... Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images. The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model. Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency. A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency. read more

Topics:

Scale-space segmentation (66%)66% related to the paper, Segmentation-based object categorization (62%)62% related to the paper, Segmentation (56%)56% related to the paper,

169 Citations

Also popular among researchers

“

SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

- Andreas Frutiger, Researcher, ETH Zurich, Institute for Biomedical Engineering

Get MS-Word and LaTeX output to any Journal within seconds

Choose a template

Select a template from a library of 40,000+ templates

Import a MS-Word file or start fresh

It takes only few seconds to import

View and edit your final output

SciSpace will automatically format your output to meet journal guidelines

Submit directly or Download

Submit to journal directly or Download in PDF, MS Word or LaTeX

(Before submission check for plagiarism via Turnitin)

Less than 3 minutes

What to expect from SciSpace?

Speed and accuracy over MS Word

With SciSpace, you do not need a word template for International Journal of Computer Assisted Radiology and Surgery.

It automatically formats your research paper to Springer formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

Time taken to format a paper and Compliance with guidelines

Plagiarism Reports via Turnitin

SciSpace has partnered with Turnitin, the leading provider of Plagiarism Check software.

Using this service, researchers can compare submissions against more than 170 million scholarly articles, a database of 70+ billion current and archived web pages. How Turnitin Integration works?

Freedom from formatting guidelines

One editor, 100K journal formats – world's largest collection of journal templates

With such a huge verified library, what you need is already there.

Easy support from all your favorite tools

International Journal of Computer Assisted Radiology and Surgery format uses SPBASIC citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

1. Can I write International Journal of Computer Assisted Radiology and Surgery in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the International Journal of Computer Assisted Radiology and Surgery guidelines and auto format it.

2. Do you follow the International Journal of Computer Assisted Radiology and Surgery guidelines?

Yes, the template is compliant with the International Journal of Computer Assisted Radiology and Surgery guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in International Journal of Computer Assisted Radiology and Surgery?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the International Journal of Computer Assisted Radiology and Surgery citation style.

4. Can I use the International Journal of Computer Assisted Radiology and Surgery templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for International Journal of Computer Assisted Radiology and Surgery.

5. Can I use a manuscript in International Journal of Computer Assisted Radiology and Surgery that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper International Journal of Computer Assisted Radiology and Surgery that you can download at the end.

6. How long does it usually take you to format my papers in International Journal of Computer Assisted Radiology and Surgery?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in International Journal of Computer Assisted Radiology and Surgery.

7. Where can I find the template for the International Journal of Computer Assisted Radiology and Surgery?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per International Journal of Computer Assisted Radiology and Surgery's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the International Journal of Computer Assisted Radiology and Surgery's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. International Journal of Computer Assisted Radiology and Surgery an online tool or is there a desktop version?

SciSpace's International Journal of Computer Assisted Radiology and Surgery is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like International Journal of Computer Assisted Radiology and Surgery?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like International Journal of Computer Assisted Radiology and Surgery?”

11. What is the output that I would get after using International Journal of Computer Assisted Radiology and Surgery?

After writing your paper autoformatting in International Journal of Computer Assisted Radiology and Surgery, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is International Journal of Computer Assisted Radiology and Surgery's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for International Journal of Computer Assisted Radiology and Surgery?

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for International Journal of Computer Assisted Radiology and Surgery. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour	Archiving policy
Green	Can archive pre-print and post-print or publisher's version/PDF
Blue	Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow	Can archive pre-print (ie pre-refereeing)
White	Archiving not formally supported

FYI:

Pre-prints as being the version of the paper before peer review and
Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In International Journal of Computer Assisted Radiology and Surgery?

The 5 most common citation types in order of usage for International Journal of Computer Assisted Radiology and Surgery are:.

S. No.	Citation Style Type
1.	Author Year
2.	Numbered
3.	Numbered (Superscripted)
4.	Author Year (Cited Pages)
5.	Footnote

15. How do I submit my article to the International Journal of Computer Assisted Radiology and Surgery?

16. Can I download International Journal of Computer Assisted Radiology and Surgery in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in International Journal of Computer Assisted Radiology and Surgery Endnote style according to Elsevier guidelines.

Fast and reliable,
built for complaince.

Instant formatting to 100% publisher guidelines on - SciSpace.

Available only on desktops 🖥

No word template required

Typset automatically formats your research paper to International Journal of Computer Assisted Radiology and Surgery formatting guidelines and citation style.

Verifed journal formats

One editor, 100K journal formats.
With the largest collection of verified journal formats, what you need is already there.

Trusted by academicians

“

I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

”

Andreas Frutiger
Researcher & Ex MS Word user

International Journal of Computer Assisted Radiology and Surgery — Template for authors

Related Journals

Journal Performance & Insights

Impact Factor

CiteRatio

2.473

5.3

Insights

Insights

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

0.701

1.408

Insights

Insights

International Journal of Computer Assisted Radiology and Surgery

International Journal of Computer Assisted Radiology and Surgery

Top papers written in this journal

Abstract:

Topics:

Abstract:

Abstract:

Topics:

Abstract:

Topics:

Abstract:

Topics:

What to expect from SciSpace?

Speed and accuracy over MS Word

Plagiarism Reports via Turnitin

Freedom from formatting guidelines

Easy support from all your favorite tools

Frequently asked questions

Fast and reliable, built for complaince.

No word template required

Verifed journal formats

Trusted by academicians

Fast and reliable,
built for complaince.