Free Fluid Detection for Blunt Abdominal Trauma Applying 3D

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Free Fluid Detection for Blunt Abdominal Trauma
Applying 3D Ultrasound
Matthias Noll11, Stergios Stergiopoulos2 and Stefan wesarg1
1
Visual Healthcare Technologies, Fraunhofer Institute for Computer Graphics
Fraunhoferstraße 5, 64283 Darmstadt, GERMANY
2
DRDC Toronto Research Centre
matthias.noll@igd.fraunhofer.de
ABSTRACT
Today approximately ¾ of the recorded combat injuries are caused by non-penetrating trauma. The
literature focus so far, has been that of trauma resuscitation, blood loss prevention - and at a second level
inflammation. However, internal bleeding caused by non-penetration trauma is malicious because the
patient does not exhibit any visible blood loss and can bleed out internally in a matter of hours. Ultrasound
trauma protocols like (e)FAST have been developed to guide physicians in detecting characteristic body
regions which will show accumulating intraperitoneal free fluids. Based on the exam the patient can be
treated according to his condition. However, the usage and interpretation of ultrasound images is dependent
on the operator’s experience. We therefore like to present an automated approach to deal with internal
bleeding detection by applying a 3D ultrasound using enhanced ultrasound software. Using the method even
unexperienced operators can be guided to produce correct acquisition location for blood detection.
Following, algorithms survey the recorded volumetric ultrasound data to detect accumulated blood within
the characteristic body regions like Morrison’s pouch, providing swift information about the patient
condition.
1.0 INTRODUCTION
Historically, combat injury types have been those of penetrating trauma. Accordingly, the majority of the
military medical literature is focusing on trauma resuscitation, blood loss prevention - and at a second level
inflammation or the like. The experience of the community in these areas is substantial. However, in modern
conflicts, ¾ of the recorded injuries are caused by non-penetrating trauma (Combat Casualty Resuscitation
Workshop, Toronto, 7-9 October 2010). These injuries are not apparent on the initial physical exam. Patients
are suffering from invisible internal bleedings rather than blood loss. Therefore, immediate medical care is
indispensable because non treated internal bleeding may lead to death within hours. Ultrasound is an
excellent tool for diagnosing internal bleeding. Characteristic internal regions like the Morison pouch can be
utilized to detect blood accumulation early on. Here, the ultrasound examination determines whether a
laparotomy is urgently required or not. However, applying ultrasound imaging is notoriously depending on
the experience of the operator (doctor, paramedics). Experience shows, that the availability of such educated
and trained personal in far-forward positions is extremely limited. Thus the question arises how expert
diagnosis can be provided in far-forward situations.
We therefore propose to extend the applied ultrasound imaging software with advanced processing
capabilities for autonomous detection of non-penetrating trauma. The proposed system applies a 3D
volumetric ultrasound probe and thus renders the required accurate positioning of traditional 2D probes at
the target location obsolete. The only requirement is an approximate probe placement at characteristic
regions defined in the FAST (Focused Assessment with Sonography in Trauma) protocol which is easy to
learn even for people without a medical background. The feasibility of this approach was determined using
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the right upper quadrant view of FAST. This view includes the kidney, the liver and Morison's pouch where
free fluid will accumulate here between organ structures. The proposed software system then analyses the
acquired ultrasound volume to determine if the kidney is included in the current view. This procedure is
continued until the kidneys location correlates with the volume center. A volumetric ray (ultrasound beam)
simulation algorithm then determines all occurrences of rib shadowing artifact. Based on this pre-processing
the probe placement might require some adjustment. Following, all visible liver vessels are automatically
detected and segmented, because these contain blood as well. The liver surface can then be extracted by
applying a textural appearance analysis on the liver vessel's convex hull. Given the already known kidney
position we have all necessary elements to ascertain if the Morrison pouch contains free fluid. Based on a
positive detection result the system then can recommend the patients immediate preparation for laparotomy.
2.0 AUTOMATED DIAGNOSTIC APPLICATIONS
Our goal is to automate the free fluid detection for emergency ultrasound. This can be one of many
applications directly available inside the ultrasound software. Enabling high performance tasks with little
training so that even untrained individuals could achieve a successful diagnosis. The ultimate goal would be
that emergency ultrasound could be applied as easily as a modern defibrillator. Due to the complexity of
scanning a patient using 2D ultrasound imaging we like to introduce a concept how to achieve detection
using the more and more spreading 3D ultrasound systems. To provide an initial concept for the software
mechanics we like to introduce a system that depends on the ultrasound emergency scanning protocol FAST.
2.1
The FAST exam
Focused Assessment with Sonography for Trauma (aka FAST) has become the quasi standard for bedside in
Ultrasound. It is ideal because the exam can be applied concurrent to other resuscitative measures.
Additionally it is safe and sensitive and can be repeated rapidly if the patient’s condition changes. The
concept behind the FAST exam is that many life-threatening injuries cause intraperitoneal bleeding. This
free fluid can be detected in the pericardial, pleural, or intraperitoneal spaces, where it tends to collect
adjacent to the organ surfaces. Here it is visible as a characteristic hypoechoic or anechoic echo region. The
bleeding is usually the result of a liver or spleen injury and commonly caused by blunt force trauma, which is
difficult to diagnose in a physical exam. The four standard views (4 P’s) of the FAST exam are:
1. Perihepatic RUQ: right kidney, right liver lobe, Morrison-Pouch, diaphragm, pleura
2. Perisplenic LUQ: left kidney, spleen, Koller-Pouch (between spleen and left kidney), diaphragm,
pleura, and pericardium
3. Pelvic: bladder, prostate, Douglas-Pouch
4. Pericardial: left liver lobe, pericardium, large vessels and pancreas
The exam is performed in the supine position. Normal findings show regular anatomy and no
intraperitoneal or intrathoracic fluid. On a high quality FAST scan about 200 ml of fluid can be reliably
detected [11]. On good images, a skilled sonographer surveying the pelvis can detect even smaller
volumes [12,13]. Since standard FAST is performed using 2 dimensional (B-mode) ultrasound, an
intimate knowledge of the relevant anatomy is required. Overall, the FAST exam is about 90% sensitive
for detecting any amount of intraperitoneal free fluid. [14]. However, it is nearly perfect for detecting
intraperitoneal bleeding in hypotensive patients who need an emergent laparotomy. It is also well
suited for diagnosing cardiac injuries from penetrating trauma [15-18]. Additionally, studies have
shown that bedside ultrasound is equal to or superior than chest radiography for identifying
hemothorax or pneumothorax in trauma patients [19-22]. For this reason FAST has been
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extended to include pneumo- and hemothorax testing [19,20,23].
Liver
Diaphragm
Kidney
Figure 1: RUQ view of FAST
2.2
The Algorithmic Approach
To determine the feasibility of the objective “automatic detection of intraperitoneal free fluids” we chose
to put the focus on the right upper quadrant (RUQ aka perihepatic or hepatorenal) view (see figure 1) of
the FAST exam. This view shows the organs liver and kidney that already have been the topic of many
literature articles. Also both organs produce good image features in ultrasound and thus strongly facilitate
the algorithmic development.
2.3
A basic approach
A basic but ultimately lacking approach for detecting free fluids in ultrasound images would be to find a
threshold value for the ultrasound intensities that are characteristic for fluid areas. Given that ultrasound
intensities are most commonly discretized using 8-Bit values [0-255], we can differentiate between 256
unique ultrasound intensities. A fluid threshold t would determine a range 1-t with t ∈ [0-255] to be fluid.
Because fluids and blood in particular have mostly hypoechoic or anechoic imaging characteristics, a good
choice for an intensity threshold would reside in the range of approximately [0-60] (see figure 2). A more
advanced thresholding approach can be observed in [24].
The Problem with the method for ultrasound lies directly in its simplistic assumptions. Blood is not the only
element generating low image intensities. Additionally, there is a difference between intraperitoneal free
fluid and intravascular blood, with both having the same intensity characteristics. Even organs like the
kidney are mostly comprised of blood vessels or tissue with a high concentration of water and therefore
appear as a dark echo region. Many different kinds of ultrasound artifacts with shadowing being one of the
most problematic for automatic fluid detection aggravate this problem even further. Shadows, which for
example start at highly absorbing or reflecting interfaces, generate low intensity responses throughout the
region behind the interface [25]. In case of a rib shadow, which generates an anechoic signal (clean shadow),
hardly any tissue information will be available behind the rib. If partial information still can be observed
(partial shadowing) it is the result of radiating ultrasound waves of neighboring transducer beams. Due to the
general dark presentation all these regions will be classified as fluid using this approach.
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Figure 2: An ultrasound image with free fluid (green arrow) and shadow (red arrow). The
thresholding result does not distinguish between shadow, fluid or background.
2.4
Towards a sophisticated approach
Advancing the basic thresholding approach to a more sophisticated algorithm requires additional information
about the structures in the field of view. These are all visible organs and their internal structures, especially
vessels (liver) and highly echogenic tissue (diaphragm). These structures can be utilized as image features to
derive the intraperitoneal spaces where free fluid will accumulate. For the RUQ view we need to detect the
Morrison’s Pouch (aka the heptorenal recess) that resides between the organ surfaces of kidney and liver. For
a person in supine position, intraperitoneal blood can accumulate here due to its deep body position. As
mentioned before the accumulating fluid can be identified as a dark echo, surrounded by the organ interfaces.
Therefore, detecting and subsequently partially or completely segmenting the organ structure is the key in
extracting the exact location of the Morrison’s Pouch and thus detect free fluid. Another approach can
incorporate ultrasound statistics or physics to derive the fluid location directly from the image intensities.
2.5
Shadow detection
As mentioned earlier, a large shadow artifact from e.g. the rib (figure 3) can render the kidney detection
process impossible. In a worst case scenario the complete kidney can disappear from the view. If the organ is
only partially visible a detection method can be misled to falsely identify kidney features (renal cortex, the
medulla and the renal capsule) at an incorrect image location. To detect these poor quality acquisitions we
have enhanced a shadow detection method by Hellier et al. [2] in some previous work [4]. Here, the
ultrasound probe geometry was determined by extracting the ultrasound probes beam surface and the shot
angles from the ultrasound view using vertical search lines. Following, ultrasound beams were simulated as
one-dimensional rays through the volume, applying the beforehand extracted probe layout. The symmetric
entropy criterion R (rupture criterion) [2] was applied to each simulated ray, resulting in signal rupture
candidates. Each signal rupture, that basically is a discontinuity measured by the mage entropy, can correlate
to a shadow artifacts starting position. Subsequently, a shadow detection test using a basic shadow model in
combination with a Leclerc estimator was applied to each rupture candidate to determine all certain shadow
starting position. The algorithm was enhanced by including the local entropy criterion by Zimmer et al. [3]
using a Rayleigh probability distribution function and a neighborhood radius auf 3 pixels to the detection
algorithm. For this, low intensity rays are detected by composing accumulating intensity rays, giving a total
intensity value (energy) for each ray. Following the energy calculation, possible shadow rays are separated
from normal rays by applying a global thresholding. The results were combined using separate global
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thresholds for shadow and tissue regions.
Figure 3: Kidney scan with large rib shadow with indicated shadow centre line (left).
Detected shadow rays in the accumulated maximum intensity image (right).
The applied thresholds of 13% and 16% of the median energy of all rays were determined for the employed
ultrasound machine using brute force methods. The result of the method is a shadow confidence map that
separates the image into the three categories, tissue, possible shadow and definitive shadow pixels. This
shadow detection method was revised and further improved to include fluid regions in the detection process.
2.6
Image Acquisition Protocol
Having a shadow detection method we envision an image acquisition pipeline, which consists of the steps
given in figure 4. The pipeline starts with a quick tutorial, where the operator must position the ultrasound
probe to capture the required view. The probe placements are derived from the FAST protocol. The correct
position is indicated as an instruction image in the software application. For the right upper quadrant view of
the FAST exam, this position is between the 8th to 11th rib along the mid-axillary line. Placing the probe here
will show the liver and the kidney as well as the Morrison’s Pouch. Concurrent to the live image generation
the system will try to automatically detect the kidney location inside the current view. If the kidney cannot be
found the ultrasound probe placement must be adjusted. This can also be indicated by a system reply like a
traffic sign, with red for an erroneous and green for a successful detection result.
Figure 4: Image acquisition pipeline with a loop for kidney and shadow detection.
This pipeline is used to obtain high quality images for image processing.
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Using the established kidney position inside the current view, angle and distance of the kidney to an
optimum acquisition position can be calculated to allow a probe placement correction by the operator. The
X- and Z-axis centre should be considered as an optimal kidney recording position during probe placement,
as all surrounding organ structures as well as the Morrison-Pouch are included in one single 3D acquisition.
2.7
Free Fluid Detection Methods
As described in the introduction, the free fluid or blood detection can’t be achieved easily. Due to the
implemented shadow detection algorithm we can increase the image quality for the automated detection.
Having a good input we can employ two and entirely different methods to it to detect free fluids. The first
method that we like to present uses the shadow detection and its temporary outputs to realize a direct fluid
detection. The method only relies on the image energy, the pixel values and the probe geometry. It generates
a confidence map that includes shadow, fluid and tissue regions. Since it does not rely on additional
assumptions about the probe placement or the image content, it is the more generalized fluid detection
method. Applying the method will also detect non free fluids in vessel structures. The second method is
based on the visible image features for the chosen FAST view. The typical image features are dependent on
the visible organ structures and their reflection, refraction and absorption capabilities. Detecting the organ
locations in the image determines the regions associated with fluid accumulation, because these are naturally
restricted by the organ surfaces.
2.7.1
Separation of Shadows and Fluids Method
The first method relies on the previously described shadow detection and thus is independent from the RUQ
FAST view. Since we already calculate the ultrasound ray energies during the shadow detection, it is
possible to separate shadow regions from fluid by introducing further assumptions about the physical
properties of the imaging system. First of all, the ultrasound wave energy and thus the wave responses at the
detector will decrease for deep tissue layers. To produce viable results for a high penetration depth, the
received signal is amplified in the ultrasound hardware. We can assume that for a normal ultrasound image
without shadows, we can achieve a good tissue representation throughout the whole image. This includes all
deep tissue layers. To recapitulate, at shadow regions we have a highly reflective surface (high intensity
responses) at the shadow origin. Behind the reflecting point, the image does not show much remaining
energy, so the generated pixels are mostly dark (anechoic). The remaining observable responses are
introduced by scatterers and ultrasound wave dispersion of neighbouring beams. Free fluids on the other
hand are mostly hypoechoic or anechoic. This means that only a small amount of beam energy is attenuated
inside the fluid region. This results in low detector responses (dark pixel values) inside the fluid region.
Figure 5: Increased through-transmission artefact (red arrows) behind a breast cyst.
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Figure 6: Denoising filters. Median (left), Gauss (centre) and Total Variation (right)
However, after the fluid region we will still see an increased returning signal (brighter signal) at normal
tissue than tissue of neighbouring rays, which did not travel through the fluid. A visualization of this
increased though-transmission artefact is show in figure 5. Applying the shadow detection, we previously
only detected shadow regions inside the image. To additionally detect fluids at the same time we remodelled
the algorithm. To start, we first reduce the ultrasound image noise in a pre-processing step. We found out,
that a good choice for a noise reduction is the total variation denoising [1], which was compared to the
standard median and Gaussian denoising functions. The visual result of the filters (figure 6) did not show
significant improvements but the detection could be enhanced further. This probably can be because of
relatively good edge preserving capabilities of the total variation filtering.
However, due to its iterative nature the processing time did increase by a factor of 2. Following the image
denoising we calculate the minimum (Min) and maximum (Max) intensity value of the denoised image.
Both values are utilized during the identification of the marginal value for the shadow candidate selection.
The marginal is determined using the following equation: Min+(Max-Min)/8. We use 1/8 of the shifted
intensity range as ray shadow candidate value to retrieve a sufficiently dark threshold that is still connected
to the actual image intensity range. For each ray we then calculate the accumulated intensity profile (see
figure 7), while ignoring every pixel that has a lower intensity than the beforehand determined marginal. To
detect fluid sections on a simulated ray, we determine for each pixel along the ray that is below the marginal
the difference between the maximum accumulated intensity (Emax) and the accumulated intensity (Ei) at that
pixel. This way we determine the remaining energy for the simulated ray at that position.
Figure 7: Accumulated ray intensity profile: Top-bottom (left),
Bottom-top (centre), Maximum Intensity image (left)
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Figure 8: Kidney image (left) with old (centre) and new (right) shadow detection result.
Visible fluid along the red slice indicator for the new detection method.
If the remaining energy happens to be larger than 1/5 of the median of all maximum ray intensities, then we
found a possible fluid pixel (region), that is marked in the confidence map. The ultrasound property that
fluids will not reduce the ray energy compared to normal tissue we see an increased through-transmission
artefact. So after the fluid region, tissue will be brighter than that of other rays at the same tissue depth.
Applying this detection process we removed the prior introduced rupture criterion R and the image entropy,
because the image noise was reduced significantly applying the total variation denoising. Using the criterion
would introduce false shadow detections in equally distributed noise regions. Also, since we are able to
differentiate between shadow and fluid sections on a simulated ray, the criterion is rendered obsolete. As for
the entropy, it does not show significant tissue characteristics after denoising. The comparable results can be
observed in figure 8. Notice the newly detected fluid in the vessel, indicated by the slicer (red).
2.7.2
Organ Detection Method
The first method uses the information of the detected kidney and liver position to search for the hyperechoic
organ interfaces as well as the diaphragm and pleura, which are visible as hyperechoic curve. Since kidney
and liver are adjacent organs due to human anatomy, the free fluid will accumulate between both organ
surfaces in the Morrison-Pouch. To determine the correct position of the Morrison-Pouch we need to detect
the approximate position of the kidney and the liver parenchyma. Optimally, we would like to segment each
organ entirely to delimit adjacent free fluid directly. The final fluid detection can be performed using
detected organ regions in combination with the shadow and fluid detection method or basic thresholding in
the ROI between both approximate organ regions.
2.7.3 Kidney detection
Due to speckle the input image has to be pre-processed to increase the image usability for the kidney
detection. The pre-processing is performed in three steps by first reducing the image size (rescaling) then
filtering the downscaled image and finally normalising the image contrast (cf. figure 9). The downscaling
seems counter-intuitive, but since the detection of the kidney does rely on the large renal cortex rather than
on small image details, the algorithms speed and reliability could be improved significantly. Also, we reduce
pixels that need filtering, because the utilized edge preserving filter is slow. Tests showed an over-linear
speedup. Using a downscaling of 70% the processing time could be reduced by 96%.
For the image improvement we use the anisotropic diffusion filtering to preserve the kidney surface edges.
The third and last pre-processing step is a contrast stretching. As the later applied edge detection utilizes
specific intensity ranges, an intra-image contrast stretching is applied. This is achieved by linear spreading of
the grayscale values between 30 and 170 to the full 8-Bit interval of 0-255.
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Figure 9: 30% scale image (left) and contrast enhanced anisotropic diffusion image (right).
Kidney features are still visible and can be detected using out strip detection algorithm.
For the actual detection, a new method was implemented to find the renal cortex. The renal cortex is
characterized by three parameters. First, there are two edges with a minimum grey value difference of 20
between the renal medulla (brighter) and the slightly darker renal cortex. The third parameter is the potential
cortex size depending on the physical image spacing. To detect the cortex area, the 3D image is analysed
slice by slice. For each 2D slice the image is searched line by line (figure 10) in y-axis direction to detect the
before mentioned grey value differences. Lines in which less than 2 edges/transitions are detected or in
which the position difference between the detected edges is larger than the possible cortex size window are
discarded. Areas fulfilling all 3 parameters (bright/dark transition, distance within the size window,
dark/bright transition) are transferred to a 2D binary image of the slice (compare figure 11). This process is
repeated for every 2D slice of the volume. In a last step binary masks are additively combined to generate a
cortex heat map for the detection.
As the heat map is a probability distribution for the renal cortex position in the volume, the brightest pixels
(where intensity = maximal value of intensity (MVOI)) are used as a seeding point for region growing with a
low level cut at (0.5 * MVIO). A first plausibility check for each region determines whether the size criterion
fits the prior size window. For too large regions the low level cut is gradually increased, small regions are
directly discarded, because they are not plausible. If no region is considered plausible, the process of region
growing is repeated with the second highest intensity value in the image. If more than one region is detected
Figure 10: Separated view of a line iteration on a single slice. Selected cortex
feature (green) and rejected region (red)
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Figure 11: Cortex detection illustrated for three volumes slices and resulting heat map (right).
we select only the best region by determining the largest remaining size and maximum number of contained
MVOIs. This automatic process makes the algorithm robust and flexible and yields good results. To improve
the region of interest and centre the segmentation seeding point as much as possible, a second detection is
performed for the renal pelvis using inverted image intensities and the already established cortex region.
The combined area of the renal cortex and pelvis are then considered as the maximum kidney 3D region of
interest (ROI). The actual segmentation is performed on the original not filtered input image. With statistical
means, the centre of the renal pelvis is determined and used as the seeding point for a radial-ray based
segmentation, which detects tissue transitions using edge features. Two results of the segmentation can be
seen in figure 12. Here, the algorithm result is compared to a ground truth segmentation.
2.7.4 Liver region detection
Since we already utilize the detected kidney location in the probe placement, we need to identify the liver as
it is the second organ shaping the Morison’s pouch. Due to the restricting view of the ultrasound probe we
only see a small portion of the right liver lobe. Nevertheless, it is usually still the largest organ structure in
the RUQ view. Strong features that characterize the liver are the liver vessel structures. Here, the interior
large vessels can be seen as round black structures. Peripheral liver vessels, which are allot smaller but
would still be visible in CT scans, can’t be observed in the ultrasound due to low image resolution, low
signal-to-noise ratio and the general existence of random speckle noise.
The large vessel structures provide good landmarks that correlate to the liver parenchyma. Given that the
available vessel structures already cover a large area of the visible liver, segmenting them will go a long way
to extract the target liver surface in kidney direction. Also, segmenting only a small portion of the liver will
determine its location in the ultrasound. Combining the result with the detected kidney location determines
directly the location of the Morrison-Pouch, which is the actual target of the fluid detection method.
Figure 12: The kidney segmentation result (red) on a 4D kidney
exam with ground though segmentation (green)
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Using methods of Frangi et al. and Sato et al. [7-8] these vessel structures can be highlighted. Both methods
detect tubular like structures throughout the image. This is done by calculating the second image derivative
though applying the hessian matrix. The results are the image’s characteristic vectors and their eigenvalues,
which can be utilized to describe object shapes inside the image. According to Frangi, a general distinction
can be made between line, plate and blob like structures. Vessels are obviously connected line structures and
can therefore be highlighted using these methods. The result is called the vesselness image (figure 11) and it
is comprised of vessel likeliness values derived by the sorted eigenvalues and eigenvectors. High values
correlate to high vessel likeliness and the other way round. Drechsler et al. [5] compared the three vesselness
methods of Frangi et al. Sato et al. and Erdt et al. [9]. Also, the usability of all methods for ultrasound was
ascertained using the initial parameters of each method. The comparison shows that Frangis method can
result in discontinued vessel segmentations, because the calculated vesselness strongly declines towards the
vessel edges. The results of Sato and Erdt are visually identical. Inspecting the resulting vesselness values,
Sato’s method generates images with higher contrast. We therefore enhanced the vessel structures using
Sato’s method with the adjustments of Drechsler et al. [6]. Following, the vessels can be segmented using
connected component region growing method using automatically placed seed points.
Since the vessel structures are only highlighted though the vesselness generation we need to extract them
through image segmentation for further processing. This was done before using semi-automatic methods
[10]. To achieve a fully automatic approach an automatic seed point placement was implemented. To
retrieve good seed points we need to consider only the highest vesselness intensities. The corresponding
regions define the highest calculated vessel likeliness. To be independent from intensity scaling we first
determine the maximum (Max) and minimum (Min) vesselness value to compute the total vesselness
intensity range (Max-Min) for the whole image.
The initial seed point placement is restricted by selecting a window that is 5% the total range. The windows
start is at the maximum image value and generates a binary segmentation of the vesselness. A connected
component analysis is then performed on the binary image, providing uniquely labelled components. We
insert a seed point to the vesselness image for each first encountered component pixel. The vessels are then
segmented using region growing on the vessel intensities with all selected seeds. In a final step we apply the
connected component analysis one last time to determine the two largest connected vessel trees that
represent the arteries and veins. This step also removes all outliers from the image that might be generated
through a faulty places seed point.
Figure 13: Vesselness image [6] showing pixels compliant with the vessel criteria (bright).
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Figure 14: Vessel segmentation (left) and the analogue 3D visualization of the vessel tree (right)
This seed point placement can be repeated iteratively by lowering the window threshold during each
iteration. Doing so requires a masking of the vesselness image with the morphologically increased
segmentation result of the previous iteration. Otherwise legions of seed points will be inserted at the
segmentation boarders exploiting the lowered threshold. The segmentation results are further enhanced in the
last step algorithm step by the use of a level set method (LSM) as described in [4]. The result of the vessel
segmentation can be observed in figure14.
To extract the liver parenchyma, which is infused by the vessel structures, we generate the convex hull on
the vessel segmentation by applying the quickhull algorithm [5]. As a result we obtain a segmentation of a
large portion of the visible liver. The currently extracted liver region can be expanded along the surface
normal to generate a better liver segmentation. This has been attempted but good results have yet to be
produced reliably. Therefore it is only mentioned here.
2.7.5 Fluid detection
After the detection and segmentation of the partial liver and kidney regions, the fluid detection should be
an easy task. We can calculate the centre of mass for both organs. Connecting both mass points with a line
gives the approximate orientation of a bounding box search area that is equivalent to the Morrison-Pouch.
This area should overlap each organ region by a small margin. Here, we should be able to detect
intraperitoneal free fluid between both partial organ segmentations (see figure 15).
Applicable methods for the segmentation:
1. The fluid segmentation can be achieved by applying a basic global thresholding in the boundary
region, as mentioned in section 2.3.
2. Applying a linear search between both organ regions. Here, low intensity values can be detected
using the image boundaries as a help structure. The intensity profile should indicate a valley
between both organ surfaces.
3. A third method that reuses already computed features is the comparison of fluid regions in the
confidence map of the shadow detection method with the detected Morrison-Pouch location. Here
we should see a fluid indication at the Morrison-Pouch.
Due to a lack of testing date this algorithm step was not yet actively tested.
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Figure 15: Two segmentation result of the detected organ regions. Kidney (red, bottom), liver
(white, top) with vessel structures (red) delimiting the Morrison-Pouch.
3.0 RESULTS
The kidney detection could be performed on 55 out of 61 available data sets of 10 healthy volunteers. This is
a 90.1% detection rate for medium to good quality images. Included in the 61 data sets is a 4D recording
with 31 single volume frames. This data set was utilized to track the kidney position relative to the probe
origin over multiple frames. A false detection did occur on 6 kidney data sets. Here, shadow artefacts or a
falsely detected kidney cortex in the heat map were the reason for the false detection. In the shadow cases the
algorithm received different results from the renal cortex and renal pelvis detection, which lead to the false
segmentation.
Qualitative evaluation was performed using the segmentation assessment in [28]. We compared the volume
difference, the relative volume difference, the dice similarity coefficient and the volume overlap (see table 1)
between all data sets. The evaluation resulted in an average volume difference of 22 pixels as well as an
average overlap of 76.39%. The dice coefficient was determined as 0.8414. The results fluctuate between the
3D and the 4D data sets. This is probably due to the increased quality of the 4D data. Its average overlap was
determined as 84.76% with an average volume difference of 15 pixels. This noticeable elevates the average
segmentation of other methods with ~70% [29]. Even the dice coefficient with 0.9083 is comparatively
higher. The average volume difference of 30 pixels with 67.7% overlap and a dice coefficient of 0.7722
reflect the relatively average result on the 3D data sets. A reason for the poor performance is the false
detection of some kidney locations. Here the segmentation did start at the wrong position.
The shadow detection could be applied to all datasets with a correctly detected rib shadow. The reason for
the shadow occurrences was a large mechanical 3D abdominal probe, which did not fit well between the ribs.
The shadow and fluid detection does generate slightly worse result as the old segmentation, when directly
compared to the reference segmentation. A value of -11.71% absolute volume difference can be explained by
the less restricted removing of darker image regions in the new algorithm. The results however look much
better for the new segmentation. Since the reference was not generated by a clinical expert, this has to be
investigated further. The fluid detection could detect large fluid regions. The method could however not be
executed on small fluid regions due to a lack of ultrasound data. Problems of the method are still partial
shadows. Here, fluid regions are falsely introduced to shadow areas.
The automatic vessel segmentation could successfully be applied to all 30 datasets containing the liver,
which are the 3D data sets only. The segmentation algorithm without the automated seed point placement
was performed on 46 additional liver data sets. All segmentations were of good quality. The generation of a
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convex hull could be performed in each scenario. Here, short vessel segmentations did result in only very
limited liver regions. This can be a problem when trying to detect the Morrison-Pouch adjacent to the
detected kidney and a too small liver region. To prevent this, the kidney must be located on the right image
side below the image centre.
Table 1: Evaluation of all data sets. Data marked with * used a falsely detection result.
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4.0 DISCUSSION
We have presented an automatic ultrasound diagnosis method to detect intraperitoneal free fluids in
patients with blunt abdominal trauma. New 3D imaging technology was applied to avoid the obvious
complex use and shortcomings of 2D ultrasound systems. All single components of the system showed
promising results, but can be enhanced further. Future work will include a complete 3D segmentation of
the kidney and the liver, giving more precise organ surface information for the Morrison-Pouch detection.
Additionally, algorithms need to be implemented that address the remaining FAST views including
hemothorax detection, leading to a fully automatic trauma diagnosis system that can be applied even by
untrained personal. Further enhancements can also be achieved with the employed shadow and fluid
detection method. Here, new ways to formalize shadow behavior and fluid influences on the physical
setup can lead to a better detection result.
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