renamed LSeg to Lseg

model_zoo/cardiac_mri_derived_left_ventricular_mass/README.md

@@ -10,7 +10,7 @@ The first model is a 3D convolutional neural network regressor ML4H<sub>reg</sub
 Here batch size, N, was 4 random samples from the training set of 3178 after excluding testing and validation samples from the total 5065 CMR images with LV mass values included in P.
 # ML4H<sub>seg</sub>
 ML4H<sub>seg</sub>, is a 3D semantic
-segmenter. To facilitate model development in the absence of hand-labeled segmentations, the models were trained with the InlineVF contours to minimize Lseg; the per-pixel cross-entropy between the label and the model’s prediction. ![LSeg](LSeg.png)
+segmenter. To facilitate model development in the absence of hand-labeled segmentations, the models were trained with the InlineVF contours to minimize Lseg; the per-pixel cross-entropy between the label and the model’s prediction. ![LSeg](Lseg.png)
 
 Here the batch size, N, was 4 from the total set of 33,071. Height, H, and width, W, are 256 voxels and there was a maximum of 13 Z slices along the short axis. There is a channel for each of the 3 labels, which were one-hot encoded in the training data, InlineVF (IVF), and probabilistic values from the softmax layer of ML4H<sub>seg</sub>. Segmentation architectures used U-Net-style long-range connections between early convolutional layers and deeper layers. Since not all CMR images used the same pixel dimensions, models were built to incorporate pixel size values with their fully connected layers before making predictions. 
 # Results