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Article ; Online: FUN-SIS: A Fully UNsupervised approach for Surgical Instrument Segmentation.

Sestini, Luca / Rosa, Benoit / De Momi, Elena / Ferrigno, Giancarlo / Padoy, Nicolas

2023 Volume 85, Page(s) 102751

Abstract: Automatic surgical instrument segmentation of endoscopic images is a crucial building block of many computer-assistance applications for minimally invasive surgery. So far, state-of-the-art approaches completely rely on the availability of a ground-truth ...

Abstract	Automatic surgical instrument segmentation of endoscopic images is a crucial building block of many computer-assistance applications for minimally invasive surgery. So far, state-of-the-art approaches completely rely on the availability of a ground-truth supervision signal, obtained via manual annotation, thus expensive to collect at large scale. In this paper, we present FUN-SIS, a Fully-UNsupervised approach for binary Surgical Instrument Segmentation. FUN-SIS trains a per-frame segmentation model on completely unlabelled endoscopic videos, by solely relying on implicit motion information and instrument shape-priors. We define shape-priors as realistic segmentation masks of the instruments, not necessarily coming from the same dataset/domain as the videos. The shape-priors can be collected in various and convenient ways, such as recycling existing annotations from other datasets. We leverage them as part of a novel generative-adversarial approach, allowing to perform unsupervised instrument segmentation of optical-flow images during training. We then use the obtained instrument masks as pseudo-labels in order to train a per-frame segmentation model; to this aim, we develop a learning-from-noisy-labels architecture, designed to extract a clean supervision signal from these pseudo-labels, leveraging their peculiar noise properties. We validate the proposed contributions on three surgical datasets, including the MICCAI 2017 EndoVis Robotic Instrument Segmentation Challenge dataset. The obtained fully-unsupervised results for surgical instrument segmentation are almost on par with the ones of fully-supervised state-of-the-art approaches. This suggests the tremendous potential of the proposed method to leverage the great amount of unlabelled data produced in the context of minimally invasive surgery.
MeSH term(s)	Humans ; Image Processing, Computer-Assisted/methods ; Endoscopy ; Surgical Instruments ; Robotics
Language	English
Publishing date	2023-01-20
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2023.102751
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Article ; Online: Dissecting self-supervised learning methods for surgical computer vision.

Ramesh, Sanat / Srivastav, Vinkle / Alapatt, Deepak / Yu, Tong / Murali, Aditya / Sestini, Luca / Nwoye, Chinedu Innocent / Hamoud, Idris / Sharma, Saurav / Fleurentin, Antoine / Exarchakis, Georgios / Karargyris, Alexandros / Padoy, Nicolas

Medical image analysis

2023 Volume 88, Page(s) 102844

Abstract: The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amounts of ...

Abstract	The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amounts of annotated data, imposing a prohibitively high cost; especially in the clinical domain. Self-Supervised Learning (SSL) methods, which have begun to gain traction in the general computer vision community, represent a potential solution to these annotation costs, allowing to learn useful representations from only unlabeled data. Still, the effectiveness of SSL methods in more complex and impactful domains, such as medicine and surgery, remains limited and unexplored. In this work, we address this critical need by investigating four state-of-the-art SSL methods (MoCo v2, SimCLR, DINO, SwAV) in the context of surgical computer vision. We present an extensive analysis of the performance of these methods on the Cholec80 dataset for two fundamental and popular tasks in surgical context understanding, phase recognition and tool presence detection. We examine their parameterization, then their behavior with respect to training data quantities in semi-supervised settings. Correct transfer of these methods to surgery, as described and conducted in this work, leads to substantial performance gains over generic uses of SSL - up to 7.4% on phase recognition and 20% on tool presence detection - as well as state-of-the-art semi-supervised phase recognition approaches by up to 14%. Further results obtained on a highly diverse selection of surgical datasets exhibit strong generalization properties. The code is available at https://github.com/CAMMA-public/SelfSupSurg.
MeSH term(s)	Humans ; Computers ; Neural Networks, Computer ; Supervised Machine Learning
Language	English
Publishing date	2023-05-24
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2023.102844
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Book ; Online: Crilin

Ceravolo, S. / Colao, F. / Curatolo, C. / Di Meco, E. / Diociaiuti, E. / Lucchesi, D. / Paesani, D. / Pastrone, N. / Saputi, A. / Sarra, I. / Sestini, L. / Tagnani, D.

A CRystal calorImeter with Longitudinal InformatioN for a future Muon Collider

2022

Abstract: The measurement of physics processes at new energy frontier experiments requires excellent spatial, time, and energy resolutions to resolve the structure of collimated high-energy jets. In a future Muon Collider, the beam-induced backgrounds (BIB) ... ...

Abstract	The measurement of physics processes at new energy frontier experiments requires excellent spatial, time, and energy resolutions to resolve the structure of collimated high-energy jets. In a future Muon Collider, the beam-induced backgrounds (BIB) represent the main challenge in the design of the detectors and of the event reconstruction algorithms. The technology and the design of the calorimeters should be chosen to reduce the effect of the BIB, while keeping good physics performance. Several requirements can be inferred: i) high granularity to reduce the overlap of BIB particles in the same calorimeter cell; ii) excellent timing (of the order of 100 ps) to reduce the out-of-time component of the BIB; iii) longitudinal segmentation to distinguish the signal showers from the fake showers produced by the BIB; iv) good energy resolution (less than 10%/sqrt(E)) to obtain good physics performance, as has been already demonstrated for conceptual particle flow calorimeters. Our proposal consists of a semi-homogeneous electromagnetic calorimeter based on Lead Fluoride Crystals (PbF2) readout by surface-mount UV-extended Silicon Photomultipliers (SiPMs): the Crilin calorimeter. In this paper, the performances of the Crilin calorimeter in the Muon Collider framework for hadron jets reconstruction have been analyzed. We report the single components characterizations together with the development of a small-scale prototype, consisting of 2 layers of 3x3 crystals each.
Keywords	Physics - Instrumentation and Detectors ; High Energy Physics - Experiment
Subject code	621
Publishing date	2022-06-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A Kinematic Bottleneck Approach For Pose Regression of Flexible Surgical Instruments directly from Images

Sestini, Luca / Rosa, Benoit / De Momi, Elena / Ferrigno, Giancarlo / Padoy, Nicolas

2021

Abstract: 3-D pose estimation of instruments is a crucial step towards automatic scene understanding in robotic minimally invasive surgery. Although robotic systems can potentially directly provide joint values, this information is not commonly exploited inside ... ...

Abstract	3-D pose estimation of instruments is a crucial step towards automatic scene understanding in robotic minimally invasive surgery. Although robotic systems can potentially directly provide joint values, this information is not commonly exploited inside the operating room, due to its possible unreliability, limited access and the time-consuming calibration required, especially for continuum robots. For this reason, standard approaches for 3-D pose estimation involve the use of external tracking systems. Recently, image-based methods have emerged as promising, non-invasive alternatives. While many image-based approaches in the literature have shown accurate results, they generally require either a complex iterative optimization for each processed image, making them unsuitable for real-time applications, or a large number of manually-annotated images for efficient learning. In this paper we propose a self-supervised image-based method, exploiting, at training time only, the imprecise kinematic information provided by the robot. In order to avoid introducing time-consuming manual annotations, the problem is formulated as an auto-encoder, smartly bottlenecked by the presence of a physical model of the robotic instruments and surgical camera, forcing a separation between image background and kinematic content. Validation of the method was performed on semi-synthetic, phantom and in-vivo datasets, obtained using a flexible robotized endoscope, showing promising results for real-time image-based 3-D pose estimation of surgical instruments.
Keywords	Computer Science - Robotics ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition
Subject code	629
Publishing date	2021-02-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Computer vision in surgery: from potential to clinical value.

Mascagni, Pietro / Alapatt, Deepak / Sestini, Luca / Altieri, Maria S / Madani, Amin / Watanabe, Yusuke / Alseidi, Adnan / Redan, Jay A / Alfieri, Sergio / Costamagna, Guido / Boškoski, Ivo / Padoy, Nicolas / Hashimoto, Daniel A

NPJ digital medicine

2022 Volume 5, Issue 1, Page(s) 163

Abstract: Hundreds of millions of operations are performed worldwide each year, and the rising uptake in minimally invasive surgery has enabled fiber optic cameras and robots to become both important tools to conduct surgery and sensors from which to capture ... ...

Abstract	Hundreds of millions of operations are performed worldwide each year, and the rising uptake in minimally invasive surgery has enabled fiber optic cameras and robots to become both important tools to conduct surgery and sensors from which to capture information about surgery. Computer vision (CV), the application of algorithms to analyze and interpret visual data, has become a critical technology through which to study the intraoperative phase of care with the goals of augmenting surgeons' decision-making processes, supporting safer surgery, and expanding access to surgical care. While much work has been performed on potential use cases, there are currently no CV tools widely used for diagnostic or therapeutic applications in surgery. Using laparoscopic cholecystectomy as an example, we reviewed current CV techniques that have been applied to minimally invasive surgery and their clinical applications. Finally, we discuss the challenges and obstacles that remain to be overcome for broader implementation and adoption of CV in surgery.
Language	English
Publishing date	2022-10-28
Publishing country	England
Document type	Journal Article ; Review
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-022-00707-5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Quantum-inspired Machine Learning on high-energy physics data

Felser, Timo / Trenti, Marco / Sestini, Lorenzo / Gianelle, Alessio / Zuliani, Davide / Lucchesi, Donatella / Montangero, Simone

2020

Abstract: Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning technique to a ... ...

Abstract	Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning technique to a very important and challenging big data problem in high energy physics: the analysis and classification of data produced by the Large Hadron Collider at CERN. In particular, we present how to effectively classify so-called b-jets, jets originating from b-quarks from proton-proton collisions in the LHCb experiment, and how to interpret the classification results. We exploit the Tensor Network approach to select important features and adapt the network geometry based on information acquired in the learning process. Finally, we show how to adapt the tree tensor network to achieve optimal precision or fast response in time without the need of repeating the learning process. These results pave the way to the implementation of high-frequency real-time applications, a key ingredient needed among others for current and future LHCb event classification able to trigger events at the tens of MHz scale. Comment: 13 pages, 4 figures
Keywords	Statistics - Machine Learning ; Condensed Matter - Statistical Mechanics ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; Physics - Data Analysis ; Statistics and Probability ; Quantum Physics
Subject code	006
Publishing date	2020-04-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Dissecting Self-Supervised Learning Methods for Surgical Computer Vision

2022

Abstract	The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amounts of annotated data, imposing a prohibitively high cost; especially in the clinical domain. Self-Supervised Learning (SSL) methods, which have begun to gain traction in the general computer vision community, represent a potential solution to these annotation costs, allowing to learn useful representations from only unlabeled data. Still, the effectiveness of SSL methods in more complex and impactful domains, such as medicine and surgery, remains limited and unexplored. In this work, we address this critical need by investigating four state-of-the-art SSL methods (MoCo v2, SimCLR, DINO, SwAV) in the context of surgical computer vision. We present an extensive analysis of the performance of these methods on the Cholec80 dataset for two fundamental and popular tasks in surgical context understanding, phase recognition and tool presence detection. We examine their parameterization, then their behavior with respect to training data quantities in semi-supervised settings. Correct transfer of these methods to surgery, as described and conducted in this work, leads to substantial performance gains over generic uses of SSL - up to 7.4% on phase recognition and 20% on tool presence detection - as well as state-of-the-art semi-supervised phase recognition approaches by up to 14%. Further results obtained on a highly diverse selection of surgical datasets exhibit strong generalization properties. The code is available at https://github.com/CAMMA-public/SelfSupSurg.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2022-07-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Unleashing the full power of LHCb to probe stealth new physics.

Borsato, M / Cid Vidal, X / Tsai, Y / Vázquez Sierra, C / Zurita, J / Alonso-Álvarez, G / Boyarsky, A / Brea Rodríguez, A / Buarque Franzosi, D / Cacciapaglia, G / Casais Vidal, A / Du, M / Elor, G / Escudero, M / Ferretti, G / Flacke, T / Foldenauer, P / Hajer, J / Henry, L /

Ilten, P / Kamenik, J / Kishor Jashal, B / Knapen, S / Kostiuk, Igor / Redi, F L / Low, M / Liu, Z / Oyanguren Campos, A / Polycarpo, E / Ramos, M / Ramos Pernas, M / Salvioni, E / Rangel, M S / Schäfer, R / Sestini, L / Soreq, Y / Tran, V Q / Timiryasov, I / van Veghel, M / Westhoff, S / Williams, M / Zupan, J

Reports on progress in physics. Physical Society (Great Britain)

2022 Volume 85, Issue 2

Abstract: In this paper, we describe the potential of the LHCb experiment to detect stealth physics. This refers to dynamics beyond the standard model that would elude searches that focus on energetic objects or precision measurements of known processes. Stealth ... ...

Abstract	In this paper, we describe the potential of the LHCb experiment to detect stealth physics. This refers to dynamics beyond the standard model that would elude searches that focus on energetic objects or precision measurements of known processes. Stealth signatures include long-lived particles and light resonances that are produced very rarely or together with overwhelming backgrounds. We will discuss why LHCb is equipped to discover this kind of physics at the Large Hadron Collider and provide examples of well-motivated theoretical models that can be probed with great detail at the experiment.
Language	English
Publishing date	2022-02-16
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	205657-4
ISSN	1361-6633 ; 0034-4885
ISSN (online)	1361-6633
ISSN	0034-4885
DOI	10.1088/1361-6633/ac4649
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Quantum computing for data analysis in high energy physics

Delgado, Andrea / Hamilton, Kathleen E. / Date, Prasanna / Vlimant, Jean-Roch / Magano, Duarte / Omar, Yasser / Bargassa, Pedrame / Francis, Anthony / Gianelle, Alessio / Sestini, Lorenzo / Lucchesi, Donatella / Zuliani, Davide / Nicotra, Davide / de Vries, Jacco / Dibenedetto, Dominica / Martinez, Miriam Lucio / Rodrigues, Eduardo / Sierra, Carlos Vazquez / Vallecorsa, Sofia /

Thaler, Jesse / Bravo-Prieto, Carlos / Chang, su Yeon / Lazar, Jeffrey / Argüelles, Carlos A. / de Lejarza, Jorge J. Martinez

2022

Abstract: Some of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the ... ...

Abstract	Some of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the Tevatron. In these facilities, the ultimate theory to describe matter at the most fundamental level is constantly probed and verified. These experiments often produce large amounts of data that require storing, processing, and analysis techniques that often push the limits of traditional information processing schemes. Thus, the High-Energy Physics (HEP) field has benefited from advancements in information processing and the development of algorithms and tools for large datasets. More recently, quantum computing applications have been investigated in an effort to understand how the community can benefit from the advantages of quantum information science. In this manuscript, we provide an overview of the state-of-the-art applications of quantum computing to data analysis in HEP, discuss the challenges and opportunities in integrating these novel analysis techniques into a day-to-day analysis workflow, and whether there is potential for a quantum advantage. Comment: 23 pages, initially submitted to Snowmass 2021
Keywords	Physics - Data Analysis ; Statistics and Probability ; High Energy Physics - Experiment ; Quantum Physics
Subject code	306
Publishing date	2022-03-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Surgical tool classification and localization

Zia, Aneeq / Bhattacharyya, Kiran / Liu, Xi / Berniker, Max / Wang, Ziheng / Nespolo, Rogerio / Kondo, Satoshi / Kasai, Satoshi / Hirasawa, Kousuke / Liu, Bo / Austin, David / Wang, Yiheng / Futrega, Michal / Puget, Jean-Francois / Li, Zhenqiang / Sato, Yoichi / Fujii, Ryo / Hachiuma, Ryo / Masuda, Mana /

Saito, Hideo / Wang, An / Xu, Mengya / Islam, Mobarakol / Bai, Long / Pang, Winnie / Ren, Hongliang / Nwoye, Chinedu / Sestini, Luca / Padoy, Nicolas / Nielsen, Maximilian / Schüttler, Samuel / Sentker, Thilo / Husseini, Hümeyra / Baltruschat, Ivo / Schmitz, Rüdiger / Werner, René / Matsun, Aleksandr / Farooq, Mugariya / Saaed, Numan / Viera, Jose Renato Restom / Yaqub, Mohammad / Getty, Neil / Xia, Fangfang / Zhao, Zixuan / Duan, Xiaotian / Yao, Xing / Lou, Ange / Yang, Hao / Han, Jintong / Noble, Jack / Wu, Jie Ying / Alshirbaji, Tamer Abdulbaki / Jalal, Nour Aldeen / Arabian, Herag / Ding, Ning / Moeller, Knut / Chen, Weiliang / He, Quan / Bilal, Muhammad / Akinosho, Taofeek / Qayyum, Adnan / Caputo, Massimo / Vohra, Hunaid / Loizou, Michael / Ajayi, Anuoluwapo / Berrou, Ilhem / Niyi-Odumosu, Faatihah / Maier-Hein, Lena / Stoyanov, Danail / Speidel, Stefanie / Jarc, Anthony

results and methods from the MICCAI 2022 SurgToolLoc challenge

2023

Abstract: The ability to automatically detect and track surgical instruments in endoscopic videos can enable transformational interventions. Assessing surgical performance and efficiency, identifying skilled tool use and choreography, and planning operational and ... ...

Abstract	The ability to automatically detect and track surgical instruments in endoscopic videos can enable transformational interventions. Assessing surgical performance and efficiency, identifying skilled tool use and choreography, and planning operational and logistical aspects of OR resources are just a few of the applications that could benefit. Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task. Annotating bounding boxes frame-by-frame is tedious and time-consuming, yet large amounts of data with a wide variety of surgical tools and surgeries must be captured for robust training. Moreover, ongoing annotator training is needed to stay up to date with surgical instrument innovation. In robotic-assisted surgery, however, potentially informative data like timestamps of instrument installation and removal can be programmatically harvested. The ability to rely on tool installation data alone would significantly reduce the workload to train robust tool-tracking models. With this motivation in mind we invited the surgical data science community to participate in the challenge, SurgToolLoc 2022. The goal was to leverage tool presence data as weak labels for machine learning models trained to detect tools and localize them in video frames with bounding boxes. We present the results of this challenge along with many of the team's efforts. We conclude by discussing these results in the broader context of machine learning and surgical data science. The training data used for this challenge consisting of 24,695 video clips with tool presence labels is also being released publicly and can be accessed at https://console.cloud.google.com/storage/browser/isi-surgtoolloc-2022.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	670
Publishing date	2023-05-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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