Computer-assisted real-time automatic prostate segmentation during TaTME: a single-center feasibility study

Kitaguchi, Daichi; Takeshita, Nobuyoshi; Matsuzaki, Hiroyuki; Hasegawa, Hiro; Honda, Ryoya; Teramura, Koichi; Ito, Masaaki

doi:10.1007/s00464-020-07659-5

Cited by 21 publications

(31 citation statements)

References 28 publications

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“…In the surgical domain, deep learning-based image recognition is applied to automatically recognize the location of critical anatomical structures, such as organs, major vessels, and surgical instruments during surgery (eg, automatic prostate recognition during transanal TME). 7 Therefore, it has the potential to be used as a fully automatic image-guided surgical system and might improve surgical safety, efficiency, and skill equalization. To the best of our knowledge, no studies thus far have reported the use of the TME navigation system using a deep learning approach.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Total Mesorectal Excision Plane Navigation in Laparoscopic Colorectal Surgery

Igaki

Kitaguchi

Shoichi

et al. 2022

Diseases of the Colon &Amp; Rectum

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See video on DCR YouTube Channel at https:// youtu.be/qVWVVrB7_1s BACKGROUND: Total mesorectal excision is the standard surgical procedure for rectal cancer because it is associated with low local recurrence rates. To the best of our knowledge, this is the first study to use an imageguided navigation system with total mesorectal excision. IMPACT OF INNOVATION:The impact of innovation is the development of a deep learning-based imageguided navigation system for areolar tissue in the total mesorectal excision plane. Such a system might be helpful to surgeons because areolar tissue can be used as a landmark for the appropriate dissection plane. TECHNOLOGY, MATERIALS, AND METHODS:This was a single-center experimental feasibility study involving 32 randomly selected patients who had undergone laparoscopic left-sided colorectal resection between 2015 and 2019. Deep learning-based semantic segmentation of areolar tissue in the total mesorectal excision plane

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Total Mesorectal Excision Plane Navigation in Laparoscopic Colorectal Surgery

Igaki

Kitaguchi

Shoichi

et al. 2022

Diseases of the Colon &Amp; Rectum

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“…Year of publication ranged from 2008 until 2021. Fourteen of the 23 studies (61%) were published in 2020 and 2021, and all used AI-based algorithms [10,11,[16][17][18][19][20][21][22][23][24][25][26][27]. The majority of studies (n = 20; 87%) used a retrospective study design, a prospective or mixed design was used in three studies [20,23,28].…”

Section: Study Characteristicsmentioning

confidence: 99%

Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review

et al. 2022

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Background Minimally invasive surgery is complex and associated with substantial learning curves. Computer-aided anatomy recognition, such as artificial intelligence-based algorithms, may improve anatomical orientation, prevent tissue injury, and improve learning curves. The study objective was to provide a comprehensive overview of current literature on the accuracy of anatomy recognition algorithms in intrathoracic and -abdominal surgery. Methods This systematic review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Pubmed, Embase, and IEEE Xplore were searched for original studies up until January 2022 on computer-aided anatomy recognition, without requiring intraoperative imaging or calibration equipment. Extracted features included surgical procedure, study population and design, algorithm type, pre-training methods, pre- and post-processing methods, data augmentation, anatomy annotation, training data, testing data, model validation strategy, goal of the algorithm, target anatomical structure, accuracy, and inference time. Results After full-text screening, 23 out of 7124 articles were included. Included studies showed a wide diversity, with six possible recognition tasks in 15 different surgical procedures, and 14 different accuracy measures used. Risk of bias in the included studies was high, especially regarding patient selection and annotation of the reference standard. Dice and intersection over union (IoU) scores of the algorithms ranged from 0.50 to 0.98 and from 74 to 98%, respectively, for various anatomy recognition tasks. High-accuracy algorithms were typically trained using larger datasets annotated by expert surgeons and focused on less-complex anatomy. Some of the high-accuracy algorithms were developed using pre-training and data augmentation. Conclusions The accuracy of included anatomy recognition algorithms varied substantially, ranging from moderate to good. Solid comparison between algorithms was complicated by the wide variety of applied methodology, target anatomical structures, and reported accuracy measures. Computer-aided intraoperative anatomy recognition is an upcoming research discipline, but still at its infancy. Larger datasets and methodological guidelines are required to improve accuracy and clinical applicability in future research. Trial registration: PROSPERO registration number: CRD42021264226

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“…During TaTME, the prostate is used as a landmark to determine the dissection line, and information on the location of the prostate can help surgeons recognize the location of the urethra and avoid urethral injury. Previously, our team developed a real‐time automatic prostate segmentation system to reduce the risk of urethral injury 29 (Figure 3). Although developing AI that can recognize anatomical structure is quite challenging compared to surgical instruments, applying semantic segmentation to anatomical structures as surgical landmarks is a promising approach; therefore, further developments are expected.…”

Section: Ai‐based Computer Visionmentioning

confidence: 99%

“…In most previous studies that applied supervised learning approach‐based CV to MIS (e.g. surgical step recognition in laparoscopic colorectal surgery, 32 laparoscopic sleeve gastrectomy, 19 and POEM 21 ; surgical instrument recognition in laparoscopic gastrectomy 23 ; and prostate recognition in TaTME 29 ), the annotation procedure was performed by two or three surgeons. Although the annotation in the HeiCo dataset was performed by non‐surgeons, i.e.…”

Section: Quality Assurance Of Annotation Datamentioning

confidence: 99%

Artificial intelligence‐based computer vision in surgery: Recent advances and future perspectives

Kitaguchi

Takeshita

Hasegawa

et al. 2021

Annals of Gastroent Surgery

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Technology has advanced surgery, especially minimally invasive surgery (MIS), including laparoscopic surgery and robotic surgery. It has led to an increase in the number of technologies in the operating room. They can provide further information about a surgical procedure, e.g. instrument usage and trajectories. Among these surgery‐related technologies, the amount of information extracted from a surgical video captured by an endoscope is especially great. Therefore, the automation of data analysis is essential in surgery to reduce the complexity of the data while maximizing its utility to enable new opportunities for research and development. Computer vision (CV) is the field of study that deals with how computers can understand digital images or videos and seeks to automate tasks that can be performed by the human visual system. Because this field deals with all the processes of real‐world information acquisition by computers, the terminology “CV” is extensive, and ranges from hardware for image sensing to AI‐based image recognition. AI‐based image recognition for simple tasks, such as recognizing snapshots, has advanced and is comparable to humans in recent years. Although surgical video recognition is a more complex and challenging task, if we can effectively apply it to MIS, it leads to future surgical advancements, such as intraoperative decision‐making support and image navigation surgery. Ultimately, automated surgery might be realized. In this article, we summarize the recent advances and future perspectives of AI‐related research and development in the field of surgery.

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Computer-assisted real-time automatic prostate segmentation during TaTME: a single-center feasibility study

Cited by 21 publications

References 28 publications

Artificial Intelligence-Based Total Mesorectal Excision Plane Navigation in Laparoscopic Colorectal Surgery

Artificial Intelligence-Based Total Mesorectal Excision Plane Navigation in Laparoscopic Colorectal Surgery

Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review

Artificial intelligence‐based computer vision in surgery: Recent advances and future perspectives

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