274 research outputs found
Ball-Scale Based Hierarchical Multi-Object Recognition in 3D Medical Images
This paper investigates, using prior shape models and the concept of ball
scale (b-scale), ways of automatically recognizing objects in 3D images without
performing elaborate searches or optimization. That is, the goal is to place
the model in a single shot close to the right pose (position, orientation, and
scale) in a given image so that the model boundaries fall in the close vicinity
of object boundaries in the image. This is achieved via the following set of
key ideas: (a) A semi-automatic way of constructing a multi-object shape model
assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship
between objects in the training images and their intensity patterns captured in
b-scale images. (c) A hierarchical mechanism of positioning the model, in a
one-shot way, in a given image from a knowledge of the learnt pose relationship
and the b-scale image of the given image to be segmented. The evaluation
results on a set of 20 routine clinical abdominal female and male CT data sets
indicate the following: (1) Incorporating a large number of objects improves
the recognition accuracy dramatically. (2) The recognition algorithm can be
thought as a hierarchical framework such that quick replacement of the model
assembly is defined as coarse recognition and delineation itself is known as
finest recognition. (3) Scale yields useful information about the relationship
between the model assembly and any given image such that the recognition
results in a placement of the model close to the actual pose without doing any
elaborate searches or optimization. (4) Effective object recognition can make
delineation most accurate.Comment: This paper was published and presented in SPIE Medical Imaging 201
Robust and fully automated segmentation of mandible from CT scans
Mandible bone segmentation from computed tomography (CT) scans is challenging
due to mandible's structural irregularities, complex shape patterns, and lack
of contrast in joints. Furthermore, connections of teeth to mandible and
mandible to remaining parts of the skull make it extremely difficult to
identify mandible boundary automatically. This study addresses these challenges
by proposing a novel framework where we define the segmentation as two
complementary tasks: recognition and delineation. For recognition, we use
random forest regression to localize mandible in 3D. For delineation, we
propose to use 3D gradient-based fuzzy connectedness (FC) image segmentation
algorithm, operating on the recognized mandible sub-volume. Despite heavy CT
artifacts and dental fillings, consisting half of the CT image data in our
experiments, we have achieved highly accurate detection and delineation
results. Specifically, detection accuracy more than 96% (measured by union of
intersection (UoI)), the delineation accuracy of 91% (measured by dice
similarity coefficient), and less than 1 mm in shape mismatch (Hausdorff
Distance) were found.Comment: 4 pages, 5 figures, IEEE International Symposium on Biomedical
Imaging (ISBI) 201
Relational Reasoning Network (RRN) for Anatomical Landmarking
Accurately identifying anatomical landmarks is a crucial step in deformation
analysis and surgical planning for craniomaxillofacial (CMF) bones. Available
methods require segmentation of the object of interest for precise landmarking.
Unlike those, our purpose in this study is to perform anatomical landmarking
using the inherent relation of CMF bones without explicitly segmenting them. We
propose a new deep network architecture, called relational reasoning network
(RRN), to accurately learn the local and the global relations of the landmarks.
Specifically, we are interested in learning landmarks in CMF region: mandible,
maxilla, and nasal bones. The proposed RRN works in an end-to-end manner,
utilizing learned relations of the landmarks based on dense-block units and
without the need for segmentation. For a given a few landmarks as input, the
proposed system accurately and efficiently localizes the remaining landmarks on
the aforementioned bones. For a comprehensive evaluation of RRN, we used
cone-beam computed tomography (CBCT) scans of 250 patients. The proposed system
identifies the landmark locations very accurately even when there are severe
pathologies or deformations in the bones. The proposed RRN has also revealed
unique relationships among the landmarks that help us infer several reasoning
about informativeness of the landmark points. RRN is invariant to order of
landmarks and it allowed us to discover the optimal configurations (number and
location) for landmarks to be localized within the object of interest
(mandible) or nearby objects (maxilla and nasal). To the best of our knowledge,
this is the first of its kind algorithm finding anatomical relations of the
objects using deep learning.Comment: 10 pages, 6 Figures, 3 Table
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans
Accurate and fast extraction of lung volumes from computed tomography (CT)
scans remains in a great demand in the clinical environment because the
available methods fail to provide a generic solution due to wide anatomical
variations of lungs and existence of pathologies. Manual annotation, current
gold standard, is time consuming and often subject to human bias. On the other
hand, current state-of-the-art fully automated lung segmentation methods fail
to make their way into the clinical practice due to their inability to
efficiently incorporate human input for handling misclassifications and praxis.
This paper presents a lung annotation tool for CT images that is interactive,
efficient, and robust. The proposed annotation tool produces an "as accurate as
possible" initial annotation based on the fuzzy-connectedness image
segmentation, followed by efficient manual fixation of the initial extraction
if deemed necessary by the practitioner. To provide maximum flexibility to the
users, our annotation tool is supported in three major operating systems
(Windows, Linux, and the Mac OS X). The quantitative results comparing our free
software with commercially available lung segmentation tools show higher degree
of consistency and precision of our software with a considerable potential to
enhance the performance of routine clinical tasks.Comment: 4 pages, 6 figures; to appear in the proceedings of 36th Annual
International Conference of the IEEE Engineering in Medicine and Biology
Society (EMBC 2014
Traces of the last earthquake sequence (1939-1944) along NAF from lacustrine sediments
Understanding the irregularity of seismic cycles: A case study in Turke
Niche differentiation is spatially and temporally regulated in the rhizosphere.
The rhizosphere is a hotspot for microbial carbon transformations, and is the entry point for root polysaccharides and polymeric carbohydrates that are important precursors to soil organic matter (SOM). However, the ecological mechanisms that underpin rhizosphere carbohydrate depolymerization are poorly understood. Using Avena fatua, a common annual grass, we analyzed time-resolved metatranscriptomes to compare microbial functions in rhizosphere, detritusphere, and combined rhizosphere-detritusphere habitats. Transcripts were binned using a unique reference database generated from soil isolate genomes, single-cell amplified genomes, metagenomes, and stable isotope probing metagenomes. While soil habitat significantly affected both community composition and overall gene expression, the succession of microbial functions occurred at a faster time scale than compositional changes. Using hierarchical clustering of upregulated decomposition genes, we identified four distinct microbial guilds populated by taxa whose functional succession patterns suggest specialization for substrates provided by fresh growing roots, decaying root detritus, the combination of live and decaying root biomass, or aging root material. Carbohydrate depolymerization genes were consistently upregulated in the rhizosphere, and both taxonomic and functional diversity were highest in the combined rhizosphere-detritusphere, suggesting coexistence of rhizosphere guilds is facilitated by niche differentiation. Metatranscriptome-defined guilds provide a framework to model rhizosphere succession and its consequences for soil carbon cycling
Explainable Transformer Prototypes for Medical Diagnoses
Deployments of artificial intelligence in medical diagnostics mandate not
just accuracy and efficacy but also trust, emphasizing the need for
explainability in machine decisions. The recent trend in automated medical
image diagnostics leans towards the deployment of Transformer-based
architectures, credited to their impressive capabilities. Since the
self-attention feature of transformers contributes towards identifying crucial
regions during the classification process, they enhance the trustability of the
methods. However, the complex intricacies of these attention mechanisms may
fall short of effectively pinpointing the regions of interest directly
influencing AI decisions. Our research endeavors to innovate a unique attention
block that underscores the correlation between 'regions' rather than 'pixels'.
To address this challenge, we introduce an innovative system grounded in
prototype learning, featuring an advanced self-attention mechanism that goes
beyond conventional ad-hoc visual explanation techniques by offering
comprehensible visual insights. A combined quantitative and qualitative
methodological approach was used to demonstrate the effectiveness of the
proposed method on the large-scale NIH chest X-ray dataset. Experimental
results showed that our proposed method offers a promising direction for
explainability, which can lead to the development of more trustable systems,
which can facilitate easier and rapid adoption of such technology into routine
clinics. The code is available at www.github.com/NUBagcilab/r2r_proto
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