  Recent advances in deep learning algorithms have led to significant benefits
for solving many medical image analysis problems. Training deep learning models
commonly requires large datasets with expert-labeled annotations. However,
acquiring expert-labeled annotation is not only expensive but also is
subjective, error-prone, and inter-/intra- observer variability introduces
noise to labels. This is particularly a problem when using deep learning models
for segmenting medical images due to the ambiguous anatomical boundaries.
Image-based medical diagnosis tools using deep learning models trained with
incorrect segmentation labels can lead to false diagnoses and treatment
suggestions. Multi-rater annotations might be better suited to train deep
learning models with small training sets compared to single-rater annotations.
The aim of this paper was to develop and evaluate a method to generate
probabilistic labels based on multi-rater annotations and anatomical knowledge
of the lesion features in MRI and a method to train segmentation models using
probabilistic labels using normalized active-passive loss as a "noise-tolerant
loss" function. The model was evaluated by comparing it to binary ground truth
for 17 knees MRI scans for clinical segmentation and detection of bone marrow
lesions (BML). The proposed method successfully improved precision 14, recall
22, and Dice score 8 percent compared to a binary cross-entropy loss function.
Overall, the results of this work suggest that the proposed normalized
active-passive loss using soft labels successfully mitigated the effects of
noisy labels.
