Convolutional Neural Network

To evaluate differences in algorithm accuracy with respect to the input field-of-view, three different architectures were created comprising of several shared networks and blocks shown in Fig. 1 (A–B). First, the entire uncropped MRI slices were used in a simple CNN classifier for determining presence or absence of ACL tear on a slice-by-slice basis. This network was based on a custom ResNet-derived architecture (Fig. ​(Fig.11 (C)) [11]. For the second network, a two-part architecture was implemented whereby an initial localization network was used to detect and generate cropped images of the cruciate ligaments, and a subsequent classifier network was used to determine presence or absence of an ACL tear. The object localization CNN was implemented as a fully convolutional network based on U-net architecture [12], while the classifier CNN was implemented with only minor modifications to the custom ResNet-derived architecture used in the first network (Fig. ​(Fig.11 (D)).

Overview of network architectures. Two convolutional neural networks (classifier, localizer) and common shared operational blocks are used in various combinations to create three different algorithms for detection of ACL tear. (a) The classifier is defined using a single 7 × 7 convolutional filter with stride 2, followed by a series of residual blocks. The resulting 4 × 4 feature map is collapsed using an average pool operation. (b) The localizer is a fully convolutional U-Net–derived architecture composed primarily of the same residual blocks used by the classifier. In the expanding pathway, the strided convolutions are replaced by convolutional transpose operations to increase feature map size. (c) In the first algorithm, entire MRI slices were used by the classifier alone to predict ACL tear. (d) In the second algorithm, an initial localizer was used to generate cropped images of the cruciate ligaments, and a subsequent classifier was used to predict ACL tear. (E) In the third algorithm, dynamically sampled randomly cropped patches without cruciate ligaments were used as an additional class for training to promote image diversity

The third network was identical to the second network; however, for the classification CNN, dynamically sampled randomly cropped patches without cruciate ligaments were also used as a new, third class for training (Fig. ​(Fig.11 (E)). Accordingly, this classification network was required to choose from one of three labels: ACL with tear, ACL without tear, and non-ACL image. Given the small number of training cases in this dataset, the addition of patches without cruciate ligaments significantly increases the diversity of training cases for network learning.

For the classifier network and the contracting pathway of the localizer network, a common shared residual block was defined by a series of 3 × 3 convolutions whose input and outputs were connected by a residual addition operation (Fig. ​(Fig.11 (A)). In each residual block, the second 3 × 3 convolution is applied with a stride of 2 along the image height and width to decrease corresponding feature maps by 50% along each dimension. In order to match the input and output feature maps, a 2 × 2 average pool is applied to the input feature map prior to addition. For the expanding pathway of the localizer network, the strided convolutions are replaced by convolutional transpose operations to expand (rather than decrease) feature map size.

The highest performing of these initial three architectures was then used as the base for experiments to evaluate differences in algorithm accuracy with respect to image dimensionality. In addition to the original 2D (single slice) input, additional networks were created using three-slice and five-slice inputs. For these 3D architectures, feature map dimensionality was decreased in the out-of-plane (anterior-posterior) direction using occasional convolutions with valid padding.