3 research outputs found
Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives
Segmentation of abdominal computed tomography(CT) provides spatial context,
morphological properties, and a framework for tissue-specific radiomics to
guide quantitative Radiological assessment. A 2015 MICCAI challenge spurred
substantial innovation in multi-organ abdominal CT segmentation with both
traditional and deep learning methods. Recent innovations in deep methods have
driven performance toward levels for which clinical translation is appealing.
However, continued cross-validation on open datasets presents the risk of
indirect knowledge contamination and could result in circular reasoning.
Moreover, 'real world' segmentations can be challenging due to the wide
variability of abdomen physiology within patients. Herein, we perform two data
retrievals to capture clinically acquired deidentified abdominal CT cohorts
with respect to a recently published variation on 3D U-Net (baseline
algorithm). First, we retrieved 2004 deidentified studies on 476 patients with
diagnosis codes involving spleen abnormalities (cohort A). Second, we retrieved
4313 deidentified studies on 1754 patients without diagnosis codes involving
spleen abnormalities (cohort B). We perform prospective evaluation of the
existing algorithm on both cohorts, yielding 13% and 8% failure rate,
respectively. Then, we identified 51 subjects in cohort A with segmentation
failures and manually corrected the liver and gallbladder labels. We re-trained
the model adding the manual labels, resulting in performance improvement of 9%
and 6% failure rate for the A and B cohorts, respectively. In summary, the
performance of the baseline on the prospective cohorts was similar to that on
previously published datasets. Moreover, adding data from the first cohort
substantively improved performance when evaluated on the second withheld
validation cohort.Comment: SPIE2020 Medical Imagin
Outlier Guided Optimization of Abdominal Segmentation
Abdominal multi-organ segmentation of computed tomography (CT) images has
been the subject of extensive research interest. It presents a substantial
challenge in medical image processing, as the shape and distribution of
abdominal organs can vary greatly among the population and within an individual
over time. While continuous integration of novel datasets into the training set
provides potential for better segmentation performance, collection of data at
scale is not only costly, but also impractical in some contexts. Moreover, it
remains unclear what marginal value additional data have to offer. Herein, we
propose a single-pass active learning method through human quality assurance
(QA). We built on a pre-trained 3D U-Net model for abdominal multi-organ
segmentation and augmented the dataset either with outlier data (e.g.,
exemplars for which the baseline algorithm failed) or inliers (e.g., exemplars
for which the baseline algorithm worked). The new models were trained using the
augmented datasets with 5-fold cross-validation (for outlier data) and withheld
outlier samples (for inlier data). Manual labeling of outliers increased Dice
scores with outliers by 0.130, compared to an increase of 0.067 with inliers
(p<0.001, two-tailed paired t-test). By adding 5 to 37 inliers or outliers to
training, we find that the marginal value of adding outliers is higher than
that of adding inliers. In summary, improvement on single-organ performance was
obtained without diminishing multi-organ performance or significantly
increasing training time. Hence, identification and correction of baseline
failure cases present an effective and efficient method of selecting training
data to improve algorithm performance.Comment: SPIE2020 Medical Imagin
Stochastic tissue window normalization of deep learning on computed tomography
Tissue window filtering has been widely used in deep learning for computed
tomography (CT) image analyses to improve training performance (e.g., soft
tissue windows for abdominal CT). However, the effectiveness of tissue window
normalization is questionable since the generalizability of the trained model
might be further harmed, especially when such models are applied to new cohorts
with different CT reconstruction kernels, contrast mechanisms, dynamic
variations in the acquisition, and physiological changes. We evaluate the
effectiveness of both with and without using soft tissue window normalization
on multisite CT cohorts. Moreover, we propose a stochastic tissue window
normalization (SWN) method to improve the generalizability of tissue window
normalization. Different from the random sampling, the SWN method centers the
randomization around the soft tissue window to maintain the specificity for
abdominal organs. To evaluate the performance of different strategies, 80
training and 453 validation and testing scans from six datasets are employed to
perform multi-organ segmentation using standard 2D U-Net. The six datasets
cover the scenarios, where the training and testing scans are from (1) same
scanner and same population, (2) same CT contrast but different pathology, and
(3) different CT contrast and pathology. The traditional soft tissue window and
nonwindowed approaches achieved better performance on (1). The proposed SWN
achieved general superior performance on (2) and (3) with statistical analyses,
which offers better generalizability for a trained model