Finding small lesions is very challenging due to lack of noticeable features, severe class imbalance, as well as the size itself. One approach to improve small lesion segmentation is to reduce the region of interest and inspect it at a higher sensitivity rather than performing it for the entire region. It is usually implemented as sequential or joint segmentation of organ and lesion, which requires additional supervision on organ segmentation. Instead, we propose to utilize an intensity distribution of a target lesion at no additional labeling cost to effectively separate regions where the lesions are possibly located from the background. It is incorporated into network training as an auxiliary task. We applied the proposed method to segmentation of small bowel carcinoid tumors in CT scans. We observed improvements for all metrics (33.5% → 38.2%, 41.3% → 47.8%, 30.0% → 35.9% for the global, per case, and per tumor Dice scores, respectively.) compared to the baseline method, which proves the validity of our idea. Our method can be one option for explicitly incorporating intensity distribution information of a target in network training.
Ascites generally manifests in the advance stage of ovarian cancer, and often mimicked by abdominal fluids such as urine in bladder. Segmentation of ascites in the pelvic region becomes increasingly challenging when the bladder is filled with urine. Anatomical location is utilized in this work to distinguish ascites from bladder. The location information is computed from a body part regressor and concatenated with contrast-enhanced computed tomography (CT) data through an embedding layer. A 3D residual U-Net is trained on the concatenated data to segment ascites and identify bladder simultaneously. 112 CT scans were used in this study; 55 of them were used for training, 20 for validation, and the remaining 37 for testing. Dice coefficient score and Jacard index are two metrics to evaluate ascites and bladder segmentation. In comparison with 3D residual U-Net, the addition of anatomical location information and two-class segmentation improved the average dice scores of ascites and urine segmentation from 0.44±0.23 to 0.51±0.16 and 0.36±0.15 to 0.40±0.13 respectively. The average volume errors of ascites and urine were reduced from 1.81±3.09 to 0.93±1.85 liters and 0.6±0.81 to 0.56±0.76 liters, respectively. These results suggested that anatomical location information and two-class segmentation are the key features to improve ascites segmentation by differentiating bladder filled with urine regions.
KEYWORDS: Computed tomography, Image segmentation, Ovarian cancer, Anatomy, Abdomen, Muscles, Chest, 3D mask effects, Cancer detection, Education and training
Ascites is often regarded as the hallmark of advanced ovarian cancer, which is the most lethal gynecologic malignancy. Ascites segmentation contributes to track the progress of ovarian cancer development by providing accurate ascites measurement, which can effectively guide subsequent treatment and potentially reduce the mortality. Segmentation of ascites is challenging due to the presence of iso-intense fluids such as bile, urine, etc., near the ascites region. In this work we propose a novel 3D U-Net segmentation method called body location embedded U-Net (BLE-U-Net) that integrates anatomical location information with the segmentation process. BLE-U-Net incorporates body part regression to predict the approximate anatomical location of each CT slice along the z- axis. The regression scores are discretized to indicate different body regions and embedded into a modified 3D U-Net to improve the ascites segmentation. Twenty contrast-enhanced body CT scans were used to evaluate the proposed method. Dice coefficients of 38 ±10 and 65 ±06 were achieved for a conventional 3D U-Net and BLE-U-Net, respectively (with t-test p <0.05). Volumes of segmented ascites were 0.51±0.74 and 0.57±0.85 liters for each method where the ground-truth volume was 0.58±0.84 liters. These results suggest that the embedded location information is the key factor to improve the ascites segmentation, which could potentially benefit ovarian cancer diagnosis and treatment.
KEYWORDS: Computed tomography, Detection and tracking algorithms, Image segmentation, Ridge detection, 3D modeling, Endoscopy, 3D scanning, Visualization, Tolerancing, System identification
We present a novel graph-theoretic method for small bowel path tracking. It is formulated as finding the minimum
cost path between given start and end nodes on a graph that is constructed based on the bowel wall detection.
We observed that a trivial solution with many short-cuts is easily made even with the wall detection, where the
tracked path penetrates indistinct walls around the contact between different parts of the small bowel. Thus, we
propose to include must-pass nodes in finding the path to better cover the entire course of the small bowel. The
proposed method does not entail training with ground-truth paths while the previous methods do. We acquired
ground-truth paths that are all connected from start to end of the small bowel for 10 abdominal CT scans,
which enables the evaluation of the path tracking for the entire course of the small bowel. The proposed method
showed clear improvements in terms of several metrics compared to the baseline method. The maximum length
of the path that is tracked without an error per scan, by the proposed method, is above 800mm on average.
We propose a novel method for nonrigid registration of coronary arteries within frames of a fluoroscopic X-ray angiogram sequence with propagated deformation field. The aim is to remove the motion of coronary arteries in order to simplify further registration of the 3D vessel structure obtained from computed tomography angiography, with the x-ray sequence. The Proposed methodology comprises two stages: propagated adjacent pairwise nonrigid registration, and, sequence-wise fixed frame nonrigid registration. In the first stage, a propagated nonrigid transformation reduces the disparity search range for each frame sequentially. In the second stage, nonrigid registration is applied for all frames with a fixed target frame, thus generating a motion-aligned sequence. Experimental evaluation conducted on a set of 7 fluoroscopic angiograms resulted in reduced target registration error, compared to previous methods, showing the effectiveness of the proposed methodology.
We present a new method for automatic detection of micro-calcifications using the Discriminative Restricted Boltzmann Machine (DRBM). The DRBM is used to automatically learn the specific features which distinguish micro-calcifications from normal tissue as well as their morphological variations. Within the DRBM, low level image structures that are specific features of micro-calcifications are automatically captured without any appropriate feature selection based on expert knowledge or time-consuming hand-tuning, which was required for previous methods. Experimental evaluation conducted on a set of 33 mammograms gave a result of area under Receiver Operating Characteristics (ROC) curve 0.8294, which showed the effectiveness of the proposed method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.