4 research outputs found
Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation
Pneumothorax is a medical emergency caused by abnormal accumulation of air in
the pleural space - the potential space between the lungs and chest wall. On 2D
chest radiographs, pneumothorax occurs within the thoracic cavity and outside
of the mediastinum and we refer to this area as "lung+ space". While deep
learning (DL) has increasingly been utilized to segment pneumothorax lesions in
chest radiographs, many existing DL models employ an end-to-end approach. These
models directly map chest radiographs to clinician-annotated lesion areas,
often neglecting the vital domain knowledge that pneumothorax is inherently
location-sensitive.
We propose a novel approach that incorporates the lung+ space as a constraint
during DL model training for pneumothorax segmentation on 2D chest radiographs.
To circumvent the need for additional annotations and to prevent potential
label leakage on the target task, our method utilizes external datasets and an
auxiliary task of lung segmentation. This approach generates a specific
constraint of lung+ space for each chest radiograph. Furthermore, we have
incorporated a discriminator to eliminate unreliable constraints caused by the
domain shift between the auxiliary and target datasets.
Our results demonstrated significant improvements, with average performance
gains of 4.6%, 3.6%, and 3.3% regarding Intersection over Union (IoU), Dice
Similarity Coefficient (DSC), and Hausdorff Distance (HD). Our research
underscores the significance of incorporating medical domain knowledge about
the location-specific nature of pneumothorax to enhance DL-based lesion
segmentation
LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images
Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8 months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source integration framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy
Automated Segmentation of CBCT Image Using Spiral CT Atlases and Convex Optimization
Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. CBCT scans have relatively low cost and low radiation dose in comparison to conventional spiral CT scans. However, a major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulties for accurate segmentation of bony structures from soft tissues, as well as separating mandible from maxilla. In this paper, we presented a novel fully automated method for CBCT image segmentation. In this method, we first estimated a patient-specific atlas using a sparse label fusion strategy from predefined spiral CT atlases. This patient-specific atlas was then integrated into a convex segmentation framework based on maximum a posteriori probability for accurate segmentation. Finally, the performance of our method was validated via comparisons with manual ground-truth segmentations. ? 2013 Springer-Verlag.EI