22 research outputs found
Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning
Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence
imaging technology that has the potential to increase intraoperative precision,
extend resection, and tailor surgery for malignant invasive brain tumors
because of its subcellular dimension resolution. Despite its promising
diagnostic potential, interpreting the gray tone fluorescence images can be
difficult for untrained users. In this review, we provide a detailed
description of bioinformatical analysis methodology of CLE images that begins
to assist the neurosurgeon and pathologist to rapidly connect on-the-fly
intraoperative imaging, pathology, and surgical observation into a
conclusionary system within the concept of theranostics. We present an overview
and discuss deep learning models for automatic detection of the diagnostic CLE
images and discuss various training regimes and ensemble modeling effect on the
power of deep learning predictive models. Two major approaches reviewed in this
paper include the models that can automatically classify CLE images into
diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and
models that can localize histological features on the CLE images using weakly
supervised methods. We also briefly review advances in the deep learning
approaches used for CLE image analysis in other organs. Significant advances in
speed and precision of automated diagnostic frame selection would augment the
diagnostic potential of CLE, improve operative workflow and integration into
brain tumor surgery. Such technology and bioinformatics analytics lend
themselves to improved precision, personalization, and theranostics in brain
tumor treatment.Comment: See the final version published in Frontiers in Oncology here:
https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful
Confocal Laser Endomicroscopy Image Analysis with Deep Convolutional Neural Networks
abstract: Rapid intraoperative diagnosis of brain tumors is of great importance for planning treatment and guiding the surgeon about the extent of resection. Currently, the standard for the preliminary intraoperative tissue analysis is frozen section biopsy that has major limitations such as tissue freezing and cutting artifacts, sampling errors, lack of immediate interaction between the pathologist and the surgeon, and time consuming.
Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation.
This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides.
These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis.Dissertation/ThesisDoctoral Dissertation Neuroscience 201
Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract
Squamous Cell Carcinoma (SCC) is the most common cancer type of the
epithelium and is often detected at a late stage. Besides invasive diagnosis of
SCC by means of biopsy and histo-pathologic assessment, Confocal Laser
Endomicroscopy (CLE) has emerged as noninvasive method that was successfully
used to diagnose SCC in vivo. For interpretation of CLE images, however,
extensive training is required, which limits its applicability and use in
clinical practice of the method. To aid diagnosis of SCC in a broader scope,
automatic detection methods have been proposed. This work compares two methods
with regard to their applicability in a transfer learning sense, i.e. training
on one tissue type (from one clinical team) and applying the learnt
classification system to another entity (different anatomy, different clinical
team). Besides a previously proposed, patch-based method based on convolutional
neural networks, a novel classification method on image level (based on a
pre-trained Inception V.3 network with dedicated preprocessing and
interpretation of class activation maps) is proposed and evaluated. The newly
presented approach improves recognition performance, yielding accuracies of
91.63% on the first data set (oral cavity) and 92.63% on a joint data set. The
generalization from oral cavity to the second data set (vocal folds) lead to
similar area-under-the-ROC curve values than a direct training on the vocal
folds data set, indicating good generalization.Comment: Erratum for version 1, correcting the number of CLE image sequences
used in one data se
Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation
Recent advances in denoising diffusion probabilistic models have shown great
success in image synthesis tasks. While there are already works exploring the
potential of this powerful tool in image semantic segmentation, its application
in weakly supervised semantic segmentation (WSSS) remains relatively
under-explored. Observing that conditional diffusion models (CDM) is capable of
generating images subject to specific distributions, in this work, we utilize
category-aware semantic information underlied in CDM to get the prediction mask
of the target object with only image-level annotations. More specifically, we
locate the desired class by approximating the derivative of the output of CDM
w.r.t the input condition. Our method is different from previous diffusion
model methods with guidance from an external classifier, which accumulates
noises in the background during the reconstruction process. Our method
outperforms state-of-the-art CAM and diffusion model methods on two public
medical image segmentation datasets, which demonstrates that CDM is a promising
tool in WSSS. Also, experiment shows our method is more time-efficient than
existing diffusion model methods, making it practical for wider applications
Deep weakly-supervised learning methods for classification and localization in histology images: a survey
Using state-of-the-art deep learning models for cancer diagnosis presents
several challenges related to the nature and availability of labeled histology
images. In particular, cancer grading and localization in these images normally
relies on both image- and pixel-level labels, the latter requiring a costly
annotation process. In this survey, deep weakly-supervised learning (WSL)
models are investigated to identify and locate diseases in histology images,
without the need for pixel-level annotations. Given training data with global
image-level labels, these models allow to simultaneously classify histology
images and yield pixel-wise localization scores, thereby identifying the
corresponding regions of interest (ROI). Since relevant WSL models have mainly
been investigated within the computer vision community, and validated on
natural scene images, we assess the extent to which they apply to histology
images which have challenging properties, e.g. very large size, similarity
between foreground/background, highly unstructured regions, stain
heterogeneity, and noisy/ambiguous labels. The most relevant models for deep
WSL are compared experimentally in terms of accuracy (classification and
pixel-wise localization) on several public benchmark histology datasets for
breast and colon cancer -- BACH ICIAR 2018, BreaKHis, CAMELYON16, and GlaS.
Furthermore, for large-scale evaluation of WSL models on histology images, we
propose a protocol to construct WSL datasets from Whole Slide Imaging. Results
indicate that several deep learning models can provide a high level of
classification accuracy, although accurate pixel-wise localization of cancer
regions remains an issue for such images. Code is publicly available.Comment: 35 pages, 18 figure
Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation
Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors. First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image. Second, due to the complexity of vascular trees and the small size of vessels, it is challenging to obtain the amount of annotated training data typically needed by deep learning methods. To address these problems, we propose a novel annotation-efficient deep learning vessel segmentation framework. The framework avoids pixel-wise annotations, only requiring weak patch-level labels to discriminate between vessel and non-vessel 2D patches in the training set, in a setup similar to the CAPTCHAs used to differentiate humans from bots in web applications. The user-provided weak annotations are used for two tasks: (1) to synthesize pixel-wise pseudo-labels for vessels and background in each patch, which are used to train a segmentation network, and (2) to train a classifier network. The classifier network allows to generate additional weak patch labels, further reducing the annotation burden, and it acts as a second opinion for poor quality images. We use this framework for the segmentation of the cerebrovascular tree in Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI). The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by ∼77% w.r.t. learning-based segmentation methods using pixel-wise labels for training