18 research outputs found
Surgical data science for safe cholecystectomy: a protocol for segmentation of hepatocystic anatomy and assessment of the critical view of safety
Minimally invasive image-guided surgery heavily relies on vision. Deep
learning models for surgical video analysis could therefore support visual
tasks such as assessing the critical view of safety (CVS) in laparoscopic
cholecystectomy (LC), potentially contributing to surgical safety and
efficiency. However, the performance, reliability and reproducibility of such
models are deeply dependent on the quality of data and annotations used in
their development. Here, we present a protocol, checklists, and visual examples
to promote consistent annotation of hepatocystic anatomy and CVS criteria. We
believe that sharing annotation guidelines can help build trustworthy
multicentric datasets for assessing generalizability of performance, thus
accelerating the clinical translation of deep learning models for surgical
video analysis.Comment: 24 pages, 34 figure
Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases
Recent advancements in deep learning methods bring computer-assistance a step
closer to fulfilling promises of safer surgical procedures. However, the
generalizability of such methods is often dependent on training on diverse
datasets from multiple medical institutions, which is a restrictive requirement
considering the sensitive nature of medical data. Recently proposed
collaborative learning methods such as Federated Learning (FL) allow for
training on remote datasets without the need to explicitly share data. Even so,
data annotation still represents a bottleneck, particularly in medicine and
surgery where clinical expertise is often required. With these constraints in
mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that
combines FL and self-supervised learning to exploit a decentralized dataset of
both labeled and unlabeled videos, thereby improving performance on the task of
surgical phase recognition. By leveraging temporal patterns in the labeled
data, FedCy helps guide unsupervised training on unlabeled data towards
learning task-specific features for phase recognition. We demonstrate
significant performance gains over state-of-the-art FSSL methods on the task of
automatic recognition of surgical phases using a newly collected
multi-institutional dataset of laparoscopic cholecystectomy videos.
Furthermore, we demonstrate that our approach also learns more generalizable
features when tested on data from an unseen domain.Comment: 11 pages, 4 figure
A Computer Vision Platform to Automatically Locate Critical Events in Surgical Videos: Documenting Safety in Laparoscopic Cholecystectomy
Temporally Constrained Neural Networks (TCNN): A framework for semi-supervised video semantic segmentation
A major obstacle to building models for effective semantic segmentation, and particularly video semantic segmentation, is a lack of large and well annotated datasets. This bottleneck is particularly prohibitive in highly specialized and regulated fields such as medicine and surgery, where video semantic segmentation could have important applications but data and expert annotations are scarce. In these settings, temporal clues and anatomical constraints could be leveraged during training to improve performance. Here, we present Temporally Constrained Neural Networks (TCNN), a semi-supervised framework used for video semantic segmentation of surgical videos. In this work, we show that autoencoder networks can be used to efficiently provide both spatial and temporal supervisory signals to train deep learning models. We test our method on a newly introduced video dataset of laparoscopic cholecystectomy procedures, Endoscapes, and an adaptation of a public dataset of cataract surgeries, CaDIS. We demonstrate that lower-dimensional representations of predicted masks can be leveraged to provide a consistent improvement on both sparsely labeled datasets with no additional computational cost at inference time. Further, the TCNN framework is model-agnostic and can be used in conjunction with other model design choices with minimal additional complexity