International Journal of Computer Assisted Radiology and Surgery

Aims & Scope

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

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.3
2024	2.30

SJR (SCImago Journal Rank)

Year	Value
2024	0.658

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	71

Journal Rank

Year	Value
2024	8719

Journal Citation Indicator

Year	Value
2024	2211

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science
• Medline

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Engineering and Medicine, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• 3D printing based on imaging data: review of medical applications

  Citation: 1227

  Authors: F., A., H., C. M., R., H.-U., F. L.

• Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer

  Citation: 932

  Authors: Juan, Aymeric, Olivier, Xavier, Bertrand

• The Medical Imaging Interaction Toolkit: challenges and advances

  Citation: 362

  Authors: Marco, Sascha, Alexander, Diana, Michael, Alfred M., Daniel, Markus, Matthias, Lena, Klaus H., Hans -Peter, Ivo

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

  Citation: 241

  Authors: Mohammadreza, Guang, Tryphon, Nigel, Timothy L., Thomas R., Franklyn A., Xujiong

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

  Citation: 237

  Authors: Fang, Fa, Peijun, Zhiyi, Dexing

• DLR MiroSurge: a versatile system for research in endoscopic telesurgery

  Citation: 229

  Authors: Ulrich, R., A., M., S., B., G., M., F., U., L., A., A., F., M., G.

• Pulmonary nodule classification with deep residual networks

  Citation: 222

  Authors: Aiden, Zhen, Dennis

• Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images

  Citation: 222

  Authors: Michał, Grzegorz, Cezary, Piotr, Łukasz, Rafał, Bogna, Krzysztof, Piotr, Andrzej

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

  Citation: 216

  Authors: Ziheng, Ann

• Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions

  Citation: 196

  Authors: Yohannes, Bingbin, Abraham Temesgen, Danail, Stamatia, Jan Hendrik, Emmanuel

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