67 research outputs found
A comprehensive survey on recent deep learning-based methods applied to surgical data
Minimally invasive surgery is highly operator dependant with a lengthy
procedural time causing fatigue to surgeon and risks to patients such as injury
to organs, infection, bleeding, and complications of anesthesia. To mitigate
such risks, real-time systems are desired to be developed that can provide
intra-operative guidance to surgeons. For example, an automated system for tool
localization, tool (or tissue) tracking, and depth estimation can enable a
clear understanding of surgical scenes preventing miscalculations during
surgical procedures. In this work, we present a systematic review of recent
machine learning-based approaches including surgical tool localization,
segmentation, tracking, and 3D scene perception. Furthermore, we provide a
detailed overview of publicly available benchmark datasets widely used for
surgical navigation tasks. While recent deep learning architectures have shown
promising results, there are still several open research problems such as a
lack of annotated datasets, the presence of artifacts in surgical scenes, and
non-textured surfaces that hinder 3D reconstruction of the anatomical
structures. Based on our comprehensive review, we present a discussion on
current gaps and needed steps to improve the adaptation of technology in
surgery.Comment: This paper is to be submitted to International journal of computer
visio
A High-level Methodology for Automatically Generating Dynamic Partially Reconfigurable Systems using IP-XACT and the UML MARTE Profile
International audienceDynamic Partial Reconfiguration (DPR) has been introduced in recent years as a method to increase the flexibility of FPGA designs. However, using DPR for building com- plex systems remains a daunting task. Recently, approaches based on Model-Driven Engi- neering (MDE) and UML MARTE standard have emerged which aim to simplify the design of complex SoCs, and in some cases, DPR systems. Nevertheless, many of these approaches lacked a standard intermediate representation to pass from high-levels of descriptions to ex- ecutable models. However, with the recent standardization of the IP-XACT specification, there is an increasing interest to use it in MDE methodologies to ease system integration and to enable design flow automation. In this paper we propose an MARTE/MDE approach which exploits the capabilities of IP-XACT to model and automatically generate DPR SoC designs. We present the MARTE modeling concepts and how these models are mapped to IP-XACT objects; the emphasis is given to the generation of IP cores that can be used in the Xilinx EDK (Embedded Design Kit) environment, since we aim to develop a complete flow around their Dynamic Partial Reconfiguration design flow. Finally, we present a case study integrating the presented concepts, showing the benefits in design efforts compared with a purely VHDL approach and using solely EDK. Experimental results show a reduction of the design efforts required to obtain the netlist required for the DPR design flow from hours required in VHDL and Xilinx EDK, to less the one hour and minutes for IP integration
SUPRA: Superpixel Guided Loss for Improved Multi-modal Segmentation in Endoscopy
Domain shift is a well-known problem in the medical imaging community. In
particular, for endoscopic image analysis where the data can have different
modalities the performance of deep learning (DL) methods gets adversely
affected. In other words, methods developed on one modality cannot be used for
a different modality. However, in real clinical settings, endoscopists switch
between modalities for better mucosal visualisation. In this paper, we explore
the domain generalisation technique to enable DL methods to be used in such
scenarios. To this extend, we propose to use super pixels generated with Simple
Linear Iterative Clustering (SLIC) which we refer to as "SUPRA" for SUPeRpixel
Augmented method. SUPRA first generates a preliminary segmentation mask making
use of our new loss "SLICLoss" that encourages both an accurate and
color-consistent segmentation. We demonstrate that SLICLoss when combined with
Binary Cross Entropy loss (BCE) can improve the model's generalisability with
data that presents significant domain shift. We validate this novel compound
loss on a vanilla U-Net using the EndoUDA dataset, which contains images for
Barret's Esophagus and polyps from two modalities. We show that our method
yields an improvement of nearly 20% in the target domain set compared to the
baseline.Comment: This work has been accepted at the LatinX in Computer Vision Research
Workshop at CVPR 202
Causal Scoring Medical Image Explanations: A Case Study On Ex-vivo Kidney Stone Images
On the promise that if human users know the cause of an output, it would
enable them to grasp the process responsible for the output, and hence provide
understanding, many explainable methods have been proposed to indicate the
cause for the output of a model based on its input. Nonetheless, little has
been reported on quantitative measurements of such causal relationships between
the inputs, the explanations, and the outputs of a model, leaving the
assessment to the user, independent of his level of expertise in the subject.
To address this situation, we explore a technique for measuring the causal
relationship between the features from the area of the object of interest in
the images of a class and the output of a classifier. Our experiments indicate
improvement in the causal relationships measured when the area of the object of
interest per class is indicated by a mask from an explainable method than when
it is indicated by human annotators. Hence the chosen name of Causal
Explanation Score (CaES
Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures
Prostate cancer is the second-most frequently diagnosed cancer and the sixth
leading cause of cancer death in males worldwide. The main problem that
specialists face during the diagnosis of prostate cancer is the localization of
Regions of Interest (ROI) containing a tumor tissue. Currently, the
segmentation of this ROI in most cases is carried out manually by expert
doctors, but the procedure is plagued with low detection rates (of about
27-44%) or overdiagnosis in some patients. Therefore, several research works
have tackled the challenge of automatically segmenting and extracting features
of the ROI from magnetic resonance images, as this process can greatly
facilitate many diagnostic and therapeutic applications. However, the lack of
clear prostate boundaries, the heterogeneity inherent to the prostate tissue,
and the variety of prostate shapes makes this process very difficult to
automate.In this work, six deep learning models were trained and analyzed with
a dataset of MRI images obtained from the Centre Hospitalaire de Dijon and
Universitat Politecnica de Catalunya. We carried out a comparison of multiple
deep learning models (i.e. U-Net, Attention U-Net, Dense-UNet, Attention
Dense-UNet, R2U-Net, and Attention R2U-Net) using categorical cross-entropy
loss function. The analysis was performed using three metrics commonly used for
image segmentation: Dice score, Jaccard index, and mean squared error. The
model that give us the best result segmenting all the zones was R2U-Net, which
achieved 0.869, 0.782, and 0.00013 for Dice, Jaccard and mean squared error,
respectively
FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation
This contribution presents a deep learning method for the segmentation of
prostate zones in MRI images based on U-Net using additive and feature pyramid
attention modules, which can improve the workflow of prostate cancer detection
and diagnosis. The proposed model is compared to seven different U-Net-based
architectures. The automatic segmentation performance of each model of the
central zone (CZ), peripheral zone (PZ), transition zone (TZ) and Tumor were
evaluated using Dice Score (DSC), and the Intersection over Union (IoU)
metrics. The proposed alternative achieved a mean DSC of 84.15% and IoU of
76.9% in the test set, outperforming most of the studied models in this work
except from R2U-Net and attention R2U-Net architectures.Comment: This paper has been accepted at the 22nd Mexican International
Conference on Artificial Intelligence (MICAI 2023
Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations
Identifying the type of kidney stones can allow urologists to determine their
cause of formation, improving the prescription of appropriate treatments to
diminish future relapses. Currently, the associated ex-vivo diagnosis (known as
Morpho-constitutional Analysis, MCA) is time-consuming, expensive and requires
a great deal of experience, as it requires a visual analysis component that is
highly operator dependant. Recently, machine learning methods have been
developed for in-vivo endoscopic stone recognition. Deep Learning (DL) based
methods outperform non-DL methods in terms of accuracy but lack explainability.
Despite this trade-off, when it comes to making high-stakes decisions, it's
important to prioritize understandable Computer-Aided Diagnosis (CADx) that
suggests a course of action based on reasonable evidence, rather than a model
prescribing a course of action. In this proposal, we learn Prototypical Parts
(PPs) per kidney stone subtype, which are used by the DL model to generate an
output classification. Using PPs in the classification task enables case-based
reasoning explanations for such output, thus making the model interpretable. In
addition, we modify global visual characteristics to describe their relevance
to the PPs and the sensitivity of our model's performance. With this, we
provide explanations with additional information at the sample, class and model
levels in contrast to previous works. Although our implementation's average
accuracy is lower than state-of-the-art (SOTA) non-interpretable DL models by
1.5 %, our models perform 2.8% better on perturbed images with a lower standard
deviation, without adversarial training. Thus, Learning PPs has the potential
to create more robust DL models.Comment: This paper has been accepted at the LatinX in Computer Vision
Research Workshop at CVPR2023 as a full paper and it will appear on the CVPR
proceeding
Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies
This contribution presents a deep learning method for the extraction and
fusion of information relating to kidney stone fragments acquired from
different viewpoints of the endoscope. Surface and section fragment images are
jointly used during the training of the classifier to improve the
discrimination power of the features by adding attention layers at the end of
each convolutional block. This approach is specifically designed to mimic the
morpho-constitutional analysis performed in ex-vivo by biologists to visually
identify kidney stones by inspecting both views. The addition of attention
mechanisms to the backbone improved the results of single view extraction
backbones by 4% on average. Moreover, in comparison to the state-of-the-art,
the fusion of the deep features improved the overall results up to 11% in terms
of kidney stone classification accuracy.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging
Endoscopy is the most widely used imaging technique for the diagnosis of
cancerous lesions in hollow organs. However, endoscopic images are often
affected by illumination artefacts: image parts may be over- or underexposed
according to the light source pose and the tissue orientation. These artifacts
have a strong negative impact on the performance of computer vision or AI-based
diagnosis tools. Although endoscopic image enhancement methods are greatly
required, little effort has been devoted to over- and under-exposition
enhancement in real-time. This contribution presents an extension to the
objective function of LMSPEC, a method originally introduced to enhance images
from natural scenes. It is used here for the exposure correction in endoscopic
imaging and the preservation of structural information. To the best of our
knowledge, this contribution is the first one that addresses the enhancement of
endoscopic images using deep learning (DL) methods. Tested on the Endo4IE
dataset, the proposed implementation has yielded a significant improvement over
LMSPEC reaching a SSIM increase of 4.40% and 4.21% for over- and underexposed
images, respectively.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
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