Auto-segmentation of thoraco-abdominal organs in pediatric dynamic MRI.

Purpose: Analysis of the abnormal motion of thoraco-abdominal organs in respiratory disorders such as the Thoracic Insufficiency Syndrome (TIS) and scoliosis such as adolescent idiopathic scoliosis (AIS) or early onset scoliosis (EOS) can lead to better surgical plans. We can use healthy subjects to find out the normal architecture and motion of a rib cage and associated organs and attempt to modify the patient's deformed anatomy to match to it. Dynamic magnetic resonance imaging (dMRI) is a practical and preferred imaging modality for capturing dynamic images of healthy pediatric subjects. In this paper, we propose an auto-segmentation set-up for the lungs, kidneys, liver, spleen, and thoraco-abdominal skin in these dMRI images which have their own challenges such as poor contrast, image non-standardness, and similarity in texture amongst gas, bone, and connective tissue at several inter-object interfaces. Methods: The segmentation set-up has been implemented in two steps: recognition and delineation using two deep neural network (DL) architectures (say DL-R and DL-D) for the recognition step and delineation step, respectively. The encoder-decoder framework in DL-D utilizes features at four different resolution levels to counter the challenges involved in the segmentation. We have evaluated on dMRI sagittal acquisitions of 189 (near-)normal subjects. The spatial resolution in all dMRI acquisitions is 1.46 mm in a sagittal slice and 6.00 mm between sagittal slices. We utilized images of 89 (10) subjects at end inspiration for training (validation). For testing we experimented with three scenarios: utilizing (1) the images of 90 (=189-89-10) different (remaining) subjects at end inspiration for testing, (2) the images of the aforementioned 90 subjects at end expiration for testing, and (3) the images of the aforesaid 99 (=89+10) subjects but at end expiration for testing. In some situations, we can take advantage of already available ground truth (GT) of a subject at a particular respiratory phase to automatically segment the object in the image of the same subject at a different respiratory phase and then refining the segmentation to create the final GT. We anticipate that this process of creating GT would require minimal post hoc correction. In this spirit, we conducted separate experiments where we assume to have the ground truth of the test subjects at end expiration for scenario (1), end inspiration for (2), and end inspiration for (3). Results: Amongst these three scenarios of testing, for the DL-R, we achieve a best average location error (LE) of about 1 voxel for the lungs, kidneys, and spleen and 1.5 voxels for the liver and the thoraco- abdominal skin. The standard deviation (SD) of LE is about 1 or 2 voxels. For the delineation approach, we achieve an average Dice coefficient (DC) of about 0.92 to 0.94 for the lungs, 0.82 for the kidneys, 0.90 for the liver, 0.81 for the spleen, and 0.93 for the thoraco-abdominal skin. The SD of DC is lower for the lungs, liver, and the thoraco-abdominal skin, and slightly higher for the spleen and kidneys. Conclusions: Motivated by applications in surgical planning for disorders such as TIS, AIS, and EOS, we have shown an auto-segmentation system for thoraco-abdominal organs in dMRI acquisitions. This proposed setup copes with the challenges posed by low resolution, motion blur, inadequate contrast, and image intensity non-standardness quite well. We are in the process of testing its effectiveness on TIS patient dMRI data.

A Hybrid Intelligence Approach for Circulating Tumor Cell Enumeration in Digital Pathology by Using CNN and Weak Annotations.

Counting the number of Circulating Tumor Cells (CTCs) for cancer screenings is currently done by cytopathologists with a heavy time and energy cost. AI, especially deep learning, has shown great potential in medical imaging domains. The aim of this paper is to develop a novel hybrid intelligence approach to automatically enumerate CTCs by combining cytopathologist expertise with the efficiency of deep learning convolutional neural networks (CNNs). This hybrid intelligence approach includes three major components: CNN based CTC detection/localization using weak annotations, CNN based CTC segmentation, and a classifier to ultimately determine CTCs. A support vector machine (SVM) was investigated for classification efficiency. The B-scale transform was also introduced to find the maximum sphericality of a given region. The SVM classifier was implemented to use a three-element vector as its input, including the B-scale (size), texture, and area values from the detection and segmentation results. We collected 466 fluoroscopic images for CTC detection/localization, 473 images for CTC segmentation and another 198 images with 323 CTCs as an independent data set for CTC enumeration. Precision and recall for CTC detection are 0.98 and 0.92, which is comparable with the state-of-the-art results that needed much larger and stricter training data sets. The counting error on an independent testing set was 2-3% and 9% (with/without B-scale) and performs much better than previous thresholding approaches with 30% of counting error rates. Recent publications prove facilitation of other types of research in object localization and segmentation are necessary.