The repeated fusion operations gated by CMSA and MSFS demonstrate improved generalizability of the network.
While accurate tracking of surgical instruments in real-time plays a crucial role in minimally invasive computer-assisted surgeries, it is a challenging task to achieve, mainly due to 1) complex surgical environment, and 2) model design with both optimal accuracy and speed.
While, today most segmentation approaches are supervised and only concentrated on a single modality dataset, this work exploits to use a target-independent unsupervised domain adaptation (UDA) technique that is capable to generalize to an unseen target modality.
Minimally invasive surgery is a surgical intervention used to examine the organs inside the abdomen and has been widely used due to its effectiveness over open surgery.
1 code implementation • 8 Jun 2021 • Sharib Ali, Debesh Jha, Noha Ghatwary, Stefano Realdon, Renato Cannizzaro, Osama E. Salem, Dominique Lamarque, Christian Daul, Michael A. Riegler, Kim V. Anonsen, Andreas Petlund, Pål Halvorsen, Jens Rittscher, Thomas de Lange, James E. East
To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset curated by a team of computational scientists and expert gastroenterologists.
Classical supervised methods commonly used often suffer from the requirement of an abudant number of training samples and are unable to generalize on unseen datasets.
Methods based on convolutional neural networks have improved the performance of biomedical image segmentation.
Ranked #1 on Medical Image Segmentation on Kvasir-SEG
To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices.
Ranked #1 on Medical Image Segmentation on KvasirCapsule-SEG
We propose an end-to-end CNN-based framework for the segmentation of stones and laser fiber.
With the increase in available large clinical and experimental datasets, there has been substantial amount of work being done on addressing the challenges in the area of biomedical image analysis.
Ranked #1 on Medical Image Segmentation on EM
Image-based tracking of laparoscopic instruments plays a fundamental role in computer and robotic-assisted surgeries by aiding surgeons and increasing patient safety.
Colonoscopy is the gold standard for examination and detection of colorectal polyps.
Our dataset consists of a total of 871 images consisting of both source and target domains.
Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks.
Ranked #9 on Medical Image Segmentation on CVC-ClinicDB
1 code implementation • 23 Oct 2020 • Debesh Jha, Sharib Ali, Krister Emanuelsen, Steven A. Hicks, VajiraThambawita, Enrique Garcia-Ceja, Michael A. Riegler, Thomas de Lange, Peter T. Schmidt, Håvard D. Johansen, Dag Johansen, Pål Halvorsen
Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development.
Ranked #2 on Medical Image Segmentation on Kvasir-Instrument
no code implementations • 12 Oct 2020 • Sharib Ali, Mariia Dmitrieva, Noha Ghatwary, Sophia Bano, Gorkem Polat, Alptekin Temizel, Adrian Krenzer, Amar Hekalo, Yun Bo Guo, Bogdan Matuszewski, Mourad Gridach, Irina Voiculescu, Vishnusai Yoganand, Arnav Chavan, Aryan Raj, Nhan T. Nguyen, Dat Q. Tran, Le Duy Huynh, Nicolas Boutry, Shahadate Rezvy, Haijian Chen, Yoon Ho Choi, Anand Subramanian, Velmurugan Balasubramanian, Xiaohong W. Gao, Hongyu Hu, Yusheng Liao, Danail Stoyanov, Christian Daul, Stefano Realdon, Renato Cannizzaro, Dominique Lamarque, Terry Tran-Nguyen, Adam Bailey, Barbara Braden, James East, Jens Rittscher
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies.
In this work, we propose to use a few-shot learning approach that requires less training data and can be used to predict label classes of test samples from an unseen dataset.
What could be more important than disease detection and localization?
To address this problem, we propose a novel approach of learning a continuous warp of the source image.
In this study, we propose to use an autoencoder for efficient video compression and fast retrieval of video images.
Removal of marker ink from these high-resolution whole slide images is non-trivial and complex problem as they contaminate different regions and in an inconsistent manner.
no code implementations • 8 May 2019 • Sharib Ali, Felix Zhou, Christian Daul, Barbara Braden, Adam Bailey, Stefano Realdon, James East, Georges Wagnières, Victor Loschenov, Enrico Grisan, Walter Blondel, Jens Rittscher
Endoscopic artifacts are a core challenge in facilitating the diagnosis and treatment of diseases in hollow organs.
Given the widespread use of endoscopy in different clinical applications, we contend that the robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem.
We introduce a new method for registration and 3D reconstruction of high- and ultra-high resolution (64 $\mu$m and 1. 3 $\mu$m pixel size) histological images of a Wistar rat brain acquired by 3D polarized light imaging (3D-PLI).